Join Us in Building the Era of Physical AI.

Recruiting Position

Robot Mechanical Design Engineer

Mechanical Design

Full-time

Mechanical DesignFull-time

What you'll be doing:

Mechanical design of robots, including but not limited to manipulator and wheeled chassis design.
Optimize the structure of robot mechanical components and follow up their production.
Complete design, process formulation and outsourced production management of transmission parts.
Troubleshoot and resolve structural design and mass-production issues of products.

What we need to see:

Bachelor’s degree or above, major in Mechanical Design or related disciplines.
Candidates with robot design or transmission structure design experience are preferred.
Capable of optimizing component structures via mechanical analysis; familiar with processing properties of common materials, mechanical manufacturing processes and standard mechanical fasteners.
Strong teamwork, communication skills, sense of responsibility and self-motivation with excellent learning ability.
Prior experience in reducer design, component mold development or mechanical competitions is a plus.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Mechanical design of robots, including but not limited to manipulator and wheeled chassis design.
Optimize the structure of robot mechanical components and follow up their production.
Complete design, process formulation and outsourced production management of transmission parts.
Troubleshoot and resolve structural design and mass-production issues of products.

What we need to see:

Bachelor’s degree or above, major in Mechanical Design or related disciplines.
Candidates with robot design or transmission structure design experience are preferred.
Capable of optimizing component structures via mechanical analysis; familiar with processing properties of common materials, mechanical manufacturing processes and standard mechanical fasteners.
Strong teamwork, communication skills, sense of responsibility and self-motivation with excellent learning ability.
Prior experience in reducer design, component mold development or mechanical competitions is a plus.

Apply：please send your resume to hr@striding.ai

Embodied AI Data Algorithm Engineer

Data

Full-time

DataFull-time

What you'll be doing:

Embodied AI Data Infrastructure: Take ownership of designing and implementing multimodal data collection, storage, and processing pipelines. This encompasses vision (egocentric and exocentric), audio, tactile, force sensing, IMU, and motion capture. Ensure scalable data acquisition, precise time-synchronization, and high-quality data annotation.
Data-to-Model Closed Loop: Drive a full-stack, data-driven paradigm covering perception, action, and feedback. Explore embodied AI modeling methodologies leveraging multi-source data—such as human demonstrations, teleoperation, and first-person videos—to provide high-quality data support for large model pre-training and VLA (Vision-Language-Action) architectures.
Data-Algorithm Co-Optimization: Construct and augment algorithm training data for humanoid robots or physical agents to enhance model generalization and robustness in real-world physical environments. Research and implement key techniques including data cleaning, deduplication, alignment, and distribution control.
Cross-Functional Collaboration: Work closely with hardware, mechanical, and product teams to seamlessly integrate interfaces across sensors, actuators, and data collection systems. Support requirements analysis for real-world business scenarios, propose feasible data acquisition and application strategies, and drive their end-to-end implementation.
Frontier Exploration: Keep abreast of the latest advancements in Embodied AI and large-scale multimodal datasets (e.g., Ego4D, EgoScale). Explore data standards, evaluation metrics, and innovative methodologies tailored to local deployment scenarios to advance data-driven Embodied AI development.

What we need to see:

Qualifications / Requirements:
Educational Background: Bachelor's degree or above in Automation, Robotics, Computer Science, Data Science, or a related technical field. A Master's degree is highly preferred.
Technical Stack: Expert proficiency in the Python data processing stack and big data ecosystems. Highly skilled in ROS/ROS2. Familiar with data services on at least one major cloud platform (e.g., AWS, GCP, Azure).
Domain Knowledge: Solid foundational understanding of core robotics algorithm domains (e.g., perception, control, and motion planning) to ensure highly effective collaboration with algorithm engineering teams.
Hardware & Sensor Expertise: Deep understanding of the integration, data characteristics, and time-synchronization principles of multi-sensor arrays (including cameras, LiDAR, IMUs, etc.).

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Embodied AI Data Infrastructure: Take ownership of designing and implementing multimodal data collection, storage, and processing pipelines. This encompasses vision (egocentric and exocentric), audio, tactile, force sensing, IMU, and motion capture. Ensure scalable data acquisition, precise time-synchronization, and high-quality data annotation.
Data-to-Model Closed Loop: Drive a full-stack, data-driven paradigm covering perception, action, and feedback. Explore embodied AI modeling methodologies leveraging multi-source data—such as human demonstrations, teleoperation, and first-person videos—to provide high-quality data support for large model pre-training and VLA (Vision-Language-Action) architectures.
Data-Algorithm Co-Optimization: Construct and augment algorithm training data for humanoid robots or physical agents to enhance model generalization and robustness in real-world physical environments. Research and implement key techniques including data cleaning, deduplication, alignment, and distribution control.
Cross-Functional Collaboration: Work closely with hardware, mechanical, and product teams to seamlessly integrate interfaces across sensors, actuators, and data collection systems. Support requirements analysis for real-world business scenarios, propose feasible data acquisition and application strategies, and drive their end-to-end implementation.
Frontier Exploration: Keep abreast of the latest advancements in Embodied AI and large-scale multimodal datasets (e.g., Ego4D, EgoScale). Explore data standards, evaluation metrics, and innovative methodologies tailored to local deployment scenarios to advance data-driven Embodied AI development.

What we need to see:

Qualifications / Requirements:
Educational Background: Bachelor's degree or above in Automation, Robotics, Computer Science, Data Science, or a related technical field. A Master's degree is highly preferred.
Technical Stack: Expert proficiency in the Python data processing stack and big data ecosystems. Highly skilled in ROS/ROS2. Familiar with data services on at least one major cloud platform (e.g., AWS, GCP, Azure).
Domain Knowledge: Solid foundational understanding of core robotics algorithm domains (e.g., perception, control, and motion planning) to ensure highly effective collaboration with algorithm engineering teams.
Hardware & Sensor Expertise: Deep understanding of the integration, data characteristics, and time-synchronization principles of multi-sensor arrays (including cameras, LiDAR, IMUs, etc.).

Apply：please send your resume to hr@striding.ai

Teleoperation Algorithm Expert

Data

Full-time

DataFull-time

What you'll be doing:

Teleoperation System & Algorithm Development: Design system architectures and develop algorithms for VR teleoperation and isomorphic master-slave teleoperation systems. Core functionalities include master-slave kinematic mapping, VR video streaming, wireless and remote teleoperation, and force/haptic feedback.
Interactive Interface Development: Develop intuitive Graphical User Interfaces (GUIs) or VR/AR interaction systems for teleoperation to elevate the operator's user experience (UX) and efficiency.
System Delivery & Performance Optimization: Collaborate cross-functionally with software, hardware, and AI teams to develop and deliver teleoperation-based data collection systems/products. Systematically optimize key performance metrics (e.g., latency, framerate, and jitter) to continuously improve the quality and efficiency of data acquisition for humanoid robots.
Full Lifecycle Product Management: Oversee the end-to-end lifecycle of hardware/software systems for remote teleoperation data collection. This spans requirements gathering, specifications, system architecture, design, prototyping, testing, and transition to production (NPI). Lead systematic root-cause analysis and drive the resolution of complex issues throughout the development cycle.
Technology Strategy: Track cutting-edge advancements in teleoperation and data collection technologies both domestically and globally. Proactively strategize on how to integrate state-of-the-art innovations to empower and advance the company’s data collection product lines.

What we need to see:

Qualifications / Requirements:
Educational Background: Master’s degree or higher in Robotics, Control Engineering, Computer Science, Electrical Engineering, or a related technical discipline.
Software Engineering: Solid experience with Linux development environments, coupled with strong programming proficiency in C++ and/or Python.
Robotics & Control: Extensive experience with the Robot Operating System (ROS/ROS2) and a deep understanding of core control algorithms (including kinematics, dynamics, and servo control).
Communication & Systems: Strong command of remote communication protocols (e.g., TCP/UDP, WebSockets) and familiarity with real-time systems development.
Bonus Qualifications: Proven experience in GUI development (e.g., Qt, Tkinter) or VR/AR interactive application development is a strong plus.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Teleoperation System & Algorithm Development: Design system architectures and develop algorithms for VR teleoperation and isomorphic master-slave teleoperation systems. Core functionalities include master-slave kinematic mapping, VR video streaming, wireless and remote teleoperation, and force/haptic feedback.
Interactive Interface Development: Develop intuitive Graphical User Interfaces (GUIs) or VR/AR interaction systems for teleoperation to elevate the operator's user experience (UX) and efficiency.
System Delivery & Performance Optimization: Collaborate cross-functionally with software, hardware, and AI teams to develop and deliver teleoperation-based data collection systems/products. Systematically optimize key performance metrics (e.g., latency, framerate, and jitter) to continuously improve the quality and efficiency of data acquisition for humanoid robots.
Full Lifecycle Product Management: Oversee the end-to-end lifecycle of hardware/software systems for remote teleoperation data collection. This spans requirements gathering, specifications, system architecture, design, prototyping, testing, and transition to production (NPI). Lead systematic root-cause analysis and drive the resolution of complex issues throughout the development cycle.
Technology Strategy: Track cutting-edge advancements in teleoperation and data collection technologies both domestically and globally. Proactively strategize on how to integrate state-of-the-art innovations to empower and advance the company’s data collection product lines.

What we need to see:

Qualifications / Requirements:
Educational Background: Master’s degree or higher in Robotics, Control Engineering, Computer Science, Electrical Engineering, or a related technical discipline.
Software Engineering: Solid experience with Linux development environments, coupled with strong programming proficiency in C++ and/or Python.
Robotics & Control: Extensive experience with the Robot Operating System (ROS/ROS2) and a deep understanding of core control algorithms (including kinematics, dynamics, and servo control).
Communication & Systems: Strong command of remote communication protocols (e.g., TCP/UDP, WebSockets) and familiarity with real-time systems development.
Bonus Qualifications: Proven experience in GUI development (e.g., Qt, Tkinter) or VR/AR interactive application development is a strong plus.

Apply：please send your resume to hr@striding.ai

Embodied AI Data Algorithm Expert

Data

Full-time

DataFull-time

What you'll be doing:

Role Overview:
Take charge of building and managing the full-stack data pipeline for Embodied AI models, encompassing data collection, cleaning, annotation, and quality assessment. Acting as the core bridge between the algorithm and data collection teams, you will ensure a continuous supply of high-quality, diverse physical interaction data to directly support the training needs of Imitation Learning (IL), Reinforcement Learning (RL), and Vision-Language-Action (VLA) models. You will have the unique opportunity to define data standards for next-generation intelligent robots, collaborate closely with top-tier Embodied AI algorithm teams to drive model iteration directly through data, and participate in building a robust data infrastructure scaling across real-robot clusters and massive compute resources.
Key Responsibilities:
Data Strategy & Architecture:
Design and implement a data pyramid architecture for Embodied AI (spanning from Foundation Skills Data -> Complex Task Data -> Long-Horizon Planning Data).
Formulate a hybrid data strategy encompassing real-world teleoperation data collection, human egocentric video data, and synthetic data generation in simulation.
Establish rigorous multimodal data standards, specifying time-synchronization protocols across vision, joint states, force/tactile feedback, and motion trajectories.
Data Collection & Annotation Management:
Build the Embodied AI data collection pipeline: Manage teleoperation teams or egocentric data collection vendors, and establish highly efficient Standard Operating Procedures (SOPs) for human-robot collaboration.
Design an automated annotation toolchain: Develop semi-automated annotation solutions leveraging SLAM, keypoint detection, and automatic segmentation to significantly reduce manual annotation costs.
Establish data causality and consistency verification mechanisms: Ensure the temporal alignment and physical plausibility of actions, visual inputs, and language instructions.
Data Quality & Compliance:
Define a comprehensive metrics system for data quality assessment, evaluating coverage, diversity, action smoothness, and physical feasibility.
Establish robust data version control and lineage tracking to fully support the reproducibility of model training.
Ensure strict privacy and compliance in data collection (e.g., anonymization/desensitization of indoor scenes, ethical review of human subject data).
Team Building & Management:
Build, scale, and manage a multifaceted data collection team (including external annotation vendors, teleoperators, and simulation engineers).
Establish a seamless data requirement alignment mechanism with the algorithm team, effectively translating abstract model training requirements into actionable data collection tasks.
Continuously optimize the Return on Investment (ROI) of data production, structurally reducing the per-unit cost of data acquisition and annotation.

What we need to see:

Qualifications / Requirements:
Educational Background: Bachelor's degree or higher in Computer Science, Artificial Intelligence, Robotics, Automation, or a closely related technical field.
Industry Experience: 5+ years of comprehensive experience in AI data operations or data engineering, including a minimum of 2 years dedicated to building data infrastructure within the Robotics, Autonomous Driving, or Embodied AI sectors.
Technical Expertise: Expert-level proficiency in designing end-to-end data collection pipelines. Familiar with standard robotics data formats and processing tools, coupled with hands-on experience using simulation platforms. Possess a solid foundation in machine learning and a deep understanding of the specific data characteristics and requirements for advanced models such as VLA, Diffusion Policy, and RT-X.
Leadership & Management: Extensive track record in project and team management. Proven ability to drive business objectives through precise project planning, optimal resource allocation, proactive risk mitigation, and seamless cross-functional collaboration. Highly capable of mentoring, guiding, and empowering team members to execute efficiently.
Passion & Drive: Maintain a strong curiosity and relentless passion for cutting-edge technologies. Eager to embrace complex challenges and push the boundaries of innovation within the Embodied AI data ecosystem.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Role Overview:
Take charge of building and managing the full-stack data pipeline for Embodied AI models, encompassing data collection, cleaning, annotation, and quality assessment. Acting as the core bridge between the algorithm and data collection teams, you will ensure a continuous supply of high-quality, diverse physical interaction data to directly support the training needs of Imitation Learning (IL), Reinforcement Learning (RL), and Vision-Language-Action (VLA) models. You will have the unique opportunity to define data standards for next-generation intelligent robots, collaborate closely with top-tier Embodied AI algorithm teams to drive model iteration directly through data, and participate in building a robust data infrastructure scaling across real-robot clusters and massive compute resources.
Key Responsibilities:
Data Strategy & Architecture:
Design and implement a data pyramid architecture for Embodied AI (spanning from Foundation Skills Data -> Complex Task Data -> Long-Horizon Planning Data).
Formulate a hybrid data strategy encompassing real-world teleoperation data collection, human egocentric video data, and synthetic data generation in simulation.
Establish rigorous multimodal data standards, specifying time-synchronization protocols across vision, joint states, force/tactile feedback, and motion trajectories.
Data Collection & Annotation Management:
Build the Embodied AI data collection pipeline: Manage teleoperation teams or egocentric data collection vendors, and establish highly efficient Standard Operating Procedures (SOPs) for human-robot collaboration.
Design an automated annotation toolchain: Develop semi-automated annotation solutions leveraging SLAM, keypoint detection, and automatic segmentation to significantly reduce manual annotation costs.
Establish data causality and consistency verification mechanisms: Ensure the temporal alignment and physical plausibility of actions, visual inputs, and language instructions.
Data Quality & Compliance:
Define a comprehensive metrics system for data quality assessment, evaluating coverage, diversity, action smoothness, and physical feasibility.
Establish robust data version control and lineage tracking to fully support the reproducibility of model training.
Ensure strict privacy and compliance in data collection (e.g., anonymization/desensitization of indoor scenes, ethical review of human subject data).
Team Building & Management:
Build, scale, and manage a multifaceted data collection team (including external annotation vendors, teleoperators, and simulation engineers).
Establish a seamless data requirement alignment mechanism with the algorithm team, effectively translating abstract model training requirements into actionable data collection tasks.
Continuously optimize the Return on Investment (ROI) of data production, structurally reducing the per-unit cost of data acquisition and annotation.

What we need to see:

Qualifications / Requirements:
Educational Background: Bachelor's degree or higher in Computer Science, Artificial Intelligence, Robotics, Automation, or a closely related technical field.
Industry Experience: 5+ years of comprehensive experience in AI data operations or data engineering, including a minimum of 2 years dedicated to building data infrastructure within the Robotics, Autonomous Driving, or Embodied AI sectors.
Technical Expertise: Expert-level proficiency in designing end-to-end data collection pipelines. Familiar with standard robotics data formats and processing tools, coupled with hands-on experience using simulation platforms. Possess a solid foundation in machine learning and a deep understanding of the specific data characteristics and requirements for advanced models such as VLA, Diffusion Policy, and RT-X.
Leadership & Management: Extensive track record in project and team management. Proven ability to drive business objectives through precise project planning, optimal resource allocation, proactive risk mitigation, and seamless cross-functional collaboration. Highly capable of mentoring, guiding, and empowering team members to execute efficiently.
Passion & Drive: Maintain a strong curiosity and relentless passion for cutting-edge technologies. Eager to embrace complex challenges and push the boundaries of innovation within the Embodied AI data ecosystem.

Apply：please send your resume to hr@striding.ai

Embodied Data Platform Architecture Expert

Data

Full-time

DataFull-time

What you'll be doing:

Role Overview:
Lead the engineering development of the Embodied AI data system and oversee the overarching data collection strategy. This is a management role where you will lead a dedicated small team. You will be responsible for architecting multimodal data processing frameworks, delivering engineered data adaptation and tuning solutions for model R&D, and designing/optimizing automated annotation algorithms and strategies.
(Note: Requires 2+ years of engineering R&D experience; prior experience with Large Language Model (LLM) data systems is highly preferred.)
Key Responsibilities:
Data Operators & Processing Framework Construction:
Operator Development: Design and implement universal, reusable data processing operators to seamlessly clean, filter, structure, and standardize raw data.
Multimodal Pipelines: Architect and build high-performance data processing pipelines tailored for multimodal inputs (e.g., text, image, audio, video) and multi-dimensional data features.
Performance Optimization: Continuously optimize data processing efficiency and computational resource utilization to robustly support large-scale data production and rapid iterations.
Internal Model Data Adaptation & Fine-Tuning Support:
Customization for R&D: Collaborate closely with model R&D teams to customize and implement specific data processing and transformation logic tailored to internal model fine-tuning requirements.
Engineering Troubleshooting: Rapidly diagnose data issues exposed during model training, fine-tuning, or evaluation processes, and provide robust, scalable engineering solutions.
Closed-Loop Optimization: Actively participate in data-model closed-loop optimization to maximize the practical performance gains and ROI models derive from high-quality data.
Data Annotation Algorithms & Strategy Management:
Automation Leadership: Take charge of the design and management of automated data annotation algorithms and workflows. This includes annotation strategies, quality assessment metrics, and the continuous building of automation capabilities.
Human-in-the-Loop (HITL) Design: Design and optimize data annotation and human-in-the-loop labeling paradigms aligned with specific model capabilities and task objectives.
Strategy Iteration: Drive the continuous iteration of annotation strategies to systematically improve labeling consistency, operational efficiency, and overall data quality.

What we need to see:

Qualifications / Requirements:
Education & Soft Skills: Bachelor's degree or higher in Computer Science or a related technical discipline. Demonstrated excellence in communication and cross-functional team collaboration.
Engineering Fundamentals: Solid programming foundation with a strong commitment to clean, efficient, and maintainable coding practices/styles.
Professional Experience: 2+ years of hands-on experience in software engineering R&D or software infrastructure development.
Bonus Qualifications (Strong Plus): Proven track record of architecting and building large-scale deep learning data systems, or directly supporting Large/Foundation Model training from a data engineering perspective.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Role Overview:
Lead the engineering development of the Embodied AI data system and oversee the overarching data collection strategy. This is a management role where you will lead a dedicated small team. You will be responsible for architecting multimodal data processing frameworks, delivering engineered data adaptation and tuning solutions for model R&D, and designing/optimizing automated annotation algorithms and strategies.
(Note: Requires 2+ years of engineering R&D experience; prior experience with Large Language Model (LLM) data systems is highly preferred.)
Key Responsibilities:
Data Operators & Processing Framework Construction:
Operator Development: Design and implement universal, reusable data processing operators to seamlessly clean, filter, structure, and standardize raw data.
Multimodal Pipelines: Architect and build high-performance data processing pipelines tailored for multimodal inputs (e.g., text, image, audio, video) and multi-dimensional data features.
Performance Optimization: Continuously optimize data processing efficiency and computational resource utilization to robustly support large-scale data production and rapid iterations.
Internal Model Data Adaptation & Fine-Tuning Support:
Customization for R&D: Collaborate closely with model R&D teams to customize and implement specific data processing and transformation logic tailored to internal model fine-tuning requirements.
Engineering Troubleshooting: Rapidly diagnose data issues exposed during model training, fine-tuning, or evaluation processes, and provide robust, scalable engineering solutions.
Closed-Loop Optimization: Actively participate in data-model closed-loop optimization to maximize the practical performance gains and ROI models derive from high-quality data.
Data Annotation Algorithms & Strategy Management:
Automation Leadership: Take charge of the design and management of automated data annotation algorithms and workflows. This includes annotation strategies, quality assessment metrics, and the continuous building of automation capabilities.
Human-in-the-Loop (HITL) Design: Design and optimize data annotation and human-in-the-loop labeling paradigms aligned with specific model capabilities and task objectives.
Strategy Iteration: Drive the continuous iteration of annotation strategies to systematically improve labeling consistency, operational efficiency, and overall data quality.

What we need to see:

Qualifications / Requirements:
Education & Soft Skills: Bachelor's degree or higher in Computer Science or a related technical discipline. Demonstrated excellence in communication and cross-functional team collaboration.
Engineering Fundamentals: Solid programming foundation with a strong commitment to clean, efficient, and maintainable coding practices/styles.
Professional Experience: 2+ years of hands-on experience in software engineering R&D or software infrastructure development.
Bonus Qualifications (Strong Plus): Proven track record of architecting and building large-scale deep learning data systems, or directly supporting Large/Foundation Model training from a data engineering perspective.

Apply：please send your resume to hr@striding.ai

Data Collection Operations Specialist

Data

Full-time

DataFull-time

What you'll be doing:

Key Responsibilities:
Base Operations & Management: Take ownership of the daily operations and overarching management of the Embodied AI Data Collection Base. Oversee the data collection team, construct standardized operational pipelines, drive the efficient generation of data assets with strict cost control, and robustly support robot model training and technological iterations.
Team Leadership & Capacity Building: Lead the recruitment, daily management, and performance evaluation of the data collection team. Organize targeted training programs (covering teleoperation rigs, the ROS framework, multimodal sensors, etc.) to continuously elevate the team's professional expertise and execution capabilities.
Process Design & Optimization: Streamline and establish an end-to-end operational framework encompassing the collection, annotation, Quality Assurance (QA), and delivery of multimodal data (including vision, tactile/force sensing, raw sensor streams, and kinematic data). Formulate comprehensive Standard Operating Procedures (SOPs) and operational guidelines.
Resource Allocation & Execution: Tailor strategies to the specific characteristics of the laboratory/base environments. Optimize project scheduling, facility utilization, hardware deployment (robot platforms, sensors, teleoperation equipment), and human resource allocation to guarantee the highly efficient execution of data collection missions.
ROI Management & Vendor Coordination: Oversee overarching project resource scheduling, cost control, and throughput efficiency. Build and refine Return on Investment (ROI) evaluation models to continuously optimize acquisition costs versus data quality. Actively manage external vendors and collaborative partners, ensuring strict adherence to project timelines, quality standards, and delivery milestones.
Cross-Functional Collaboration & Requirements Management: Partner seamlessly with algorithm, R&D, and product teams to crystalize data requirements. Drive the tight integration between data collection strategies and the model training closed-loop, maximizing the impact of data in supporting downstream perception, planning/decision-making, and control modules.
Data Security & Compliance: Architect and enforce strict compliance operations mechanisms. Implement rigorous protocols for data anonymization/desensitization, confidentiality, and lineage traceability. Ensure the entire lifecycle of data collection, storage, and utilization strictly adheres to legal regulations and industry compliance standards.
Technology & Process Iteration: Keep a pulse on industry-leading data collection technologies and toolchains (e.g., next-gen teleoperation rigs, advanced data platforms). Proactively champion process automation and the scaling up of operational capabilities.

What we need to see:

Qualifications / Requirements:
Educational Background: Bachelor's degree or higher in Computer Science, Automation, Robotics, Artificial Intelligence, Data Science, or a related technical discipline.
Industry Experience: 2+ years of management experience in data collection operations, data engineering, or data annotation. Candidates with an industry background in Embodied AI, Robotics, Autonomous Driving, or 3D Vision are highly preferred.
Team Leadership: Proven track record in team management, possessing the capability to independently build, train, and conduct performance evaluations for data collection task forces.
Operational Acumen: Prior experience in facility/base operations, strict cost control, and ROI (Return on Investment) management is strongly preferred.
Project Management: Excellent project management, cross-functional communication, and resource coordination skills. Capable of independently championing and driving large-scale data collection initiatives from end to end.
Compliance & Security: Solid understanding of data compliance and privacy protection laws/regulations, coupled with a rigorous awareness of data security protocols.
Bonus Qualifications (Strong Plus):
Hardware/Tech Familiarity: Familiarity with technical solutions involving teleoperation rigs, force/haptic feedback, and tactile sensors.
Toolchain Integration: Hands-on experience with the deployment, onboarding, or integration of comprehensive data platforms and annotation toolchains.
Scale Experience: A proven track record of successfully executing massive-scale data collection projects specifically within the Autonomous Driving or Embodied AI domains.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Key Responsibilities:
Base Operations & Management: Take ownership of the daily operations and overarching management of the Embodied AI Data Collection Base. Oversee the data collection team, construct standardized operational pipelines, drive the efficient generation of data assets with strict cost control, and robustly support robot model training and technological iterations.
Team Leadership & Capacity Building: Lead the recruitment, daily management, and performance evaluation of the data collection team. Organize targeted training programs (covering teleoperation rigs, the ROS framework, multimodal sensors, etc.) to continuously elevate the team's professional expertise and execution capabilities.
Process Design & Optimization: Streamline and establish an end-to-end operational framework encompassing the collection, annotation, Quality Assurance (QA), and delivery of multimodal data (including vision, tactile/force sensing, raw sensor streams, and kinematic data). Formulate comprehensive Standard Operating Procedures (SOPs) and operational guidelines.
Resource Allocation & Execution: Tailor strategies to the specific characteristics of the laboratory/base environments. Optimize project scheduling, facility utilization, hardware deployment (robot platforms, sensors, teleoperation equipment), and human resource allocation to guarantee the highly efficient execution of data collection missions.
ROI Management & Vendor Coordination: Oversee overarching project resource scheduling, cost control, and throughput efficiency. Build and refine Return on Investment (ROI) evaluation models to continuously optimize acquisition costs versus data quality. Actively manage external vendors and collaborative partners, ensuring strict adherence to project timelines, quality standards, and delivery milestones.
Cross-Functional Collaboration & Requirements Management: Partner seamlessly with algorithm, R&D, and product teams to crystalize data requirements. Drive the tight integration between data collection strategies and the model training closed-loop, maximizing the impact of data in supporting downstream perception, planning/decision-making, and control modules.
Data Security & Compliance: Architect and enforce strict compliance operations mechanisms. Implement rigorous protocols for data anonymization/desensitization, confidentiality, and lineage traceability. Ensure the entire lifecycle of data collection, storage, and utilization strictly adheres to legal regulations and industry compliance standards.
Technology & Process Iteration: Keep a pulse on industry-leading data collection technologies and toolchains (e.g., next-gen teleoperation rigs, advanced data platforms). Proactively champion process automation and the scaling up of operational capabilities.

What we need to see:

Qualifications / Requirements:
Educational Background: Bachelor's degree or higher in Computer Science, Automation, Robotics, Artificial Intelligence, Data Science, or a related technical discipline.
Industry Experience: 2+ years of management experience in data collection operations, data engineering, or data annotation. Candidates with an industry background in Embodied AI, Robotics, Autonomous Driving, or 3D Vision are highly preferred.
Team Leadership: Proven track record in team management, possessing the capability to independently build, train, and conduct performance evaluations for data collection task forces.
Operational Acumen: Prior experience in facility/base operations, strict cost control, and ROI (Return on Investment) management is strongly preferred.
Project Management: Excellent project management, cross-functional communication, and resource coordination skills. Capable of independently championing and driving large-scale data collection initiatives from end to end.
Compliance & Security: Solid understanding of data compliance and privacy protection laws/regulations, coupled with a rigorous awareness of data security protocols.
Bonus Qualifications (Strong Plus):
Hardware/Tech Familiarity: Familiarity with technical solutions involving teleoperation rigs, force/haptic feedback, and tactile sensors.
Toolchain Integration: Hands-on experience with the deployment, onboarding, or integration of comprehensive data platforms and annotation toolchains.
Scale Experience: A proven track record of successfully executing massive-scale data collection projects specifically within the Autonomous Driving or Embodied AI domains.

Apply：please send your resume to hr@striding.ai

Embodied Data Platform Engineer

Data

Full-time

DataFull-time

What you'll be doing:

Key Responsibilities:
Backend Development & Maintenance: Take ownership of the robust development and ongoing maintenance of backend services for the Embodied AI Data Platform.
Architecture Design & Optimization: Architect and continuously optimize the platform's backend infrastructure. Core technical domains include data ingestion pipelines, scalable storage solutions, data lifecycle management, distributed task scheduling, and rigorous data security/governance.
Cross-Functional Delivery: Collaborate seamlessly with product, algorithm, and business teams. Drive highly efficient communication and agile delivery, ensuring that backend technical implementations align perfectly with complex business and algorithmic requirements.
Technology Exploration: Keep a pulse on cutting-edge backend and data engineering technologies. Proactively explore and integrate innovative technical solutions to address complex challenges within real-world Embodied AI business scenarios.

What we need to see:

Qualifications / Requirements:
Educational & Professional Background: Bachelor’s degree or higher in Computer Science, Software Engineering, or a related technical discipline, coupled with 3+ years of proven experience in backend software development.
Core Tech Stack: Expert-level proficiency in Python and solid experience with backend frameworks (e.g., FastAPI). Highly skilled in deploying and maintaining robust data components and infrastructure, including MongoDB, PostgreSQL, Redis, and Kubernetes (K8s). Deep familiarity with containerization technologies and major cloud computing platforms.
System Architecture Expertise: Proven track record of architecting and launching large-scale data platforms. Deep understanding of high-concurrency and high-availability (HA) system design patterns.
Soft Skills & Product Mindset: Strong product sense, coupled with exceptional cross-functional collaboration capabilities and a highly self-driven capacity for rapid learning.
Bonus Qualifications (Strong Plus): Hands-on experience developing data products tailored for Deep Learning, Embodied AI, or Large/Foundation Models is highly preferred.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Key Responsibilities:
Backend Development & Maintenance: Take ownership of the robust development and ongoing maintenance of backend services for the Embodied AI Data Platform.
Architecture Design & Optimization: Architect and continuously optimize the platform's backend infrastructure. Core technical domains include data ingestion pipelines, scalable storage solutions, data lifecycle management, distributed task scheduling, and rigorous data security/governance.
Cross-Functional Delivery: Collaborate seamlessly with product, algorithm, and business teams. Drive highly efficient communication and agile delivery, ensuring that backend technical implementations align perfectly with complex business and algorithmic requirements.
Technology Exploration: Keep a pulse on cutting-edge backend and data engineering technologies. Proactively explore and integrate innovative technical solutions to address complex challenges within real-world Embodied AI business scenarios.

What we need to see:

Qualifications / Requirements:
Educational & Professional Background: Bachelor’s degree or higher in Computer Science, Software Engineering, or a related technical discipline, coupled with 3+ years of proven experience in backend software development.
Core Tech Stack: Expert-level proficiency in Python and solid experience with backend frameworks (e.g., FastAPI). Highly skilled in deploying and maintaining robust data components and infrastructure, including MongoDB, PostgreSQL, Redis, and Kubernetes (K8s). Deep familiarity with containerization technologies and major cloud computing platforms.
System Architecture Expertise: Proven track record of architecting and launching large-scale data platforms. Deep understanding of high-concurrency and high-availability (HA) system design patterns.
Soft Skills & Product Mindset: Strong product sense, coupled with exceptional cross-functional collaboration capabilities and a highly self-driven capacity for rapid learning.
Bonus Qualifications (Strong Plus): Hands-on experience developing data products tailored for Deep Learning, Embodied AI, or Large/Foundation Models is highly preferred.

Apply：please send your resume to hr@striding.ai

Multimodal Perception Algorithm Engineer (Embodied AI)

Embodied AI

Full-time/Internship

Embodied AIFull-time/Internship

What you'll be doing:

Participate in the R&D of multimodal perception and 3D reconstruction algorithms for embodied robots, focusing on 3D point cloud reconstruction and dynamic scene modeling.
Construct map representations tailored for embodied AI, and develop semantic-level 3D reconstruction algorithms (e.g., semantic point clouds, occupancy grids, 3D scene graphs).
Contribute to the engineering deployment of point cloud processing, semantic segmentation, and reconstruction algorithms on edge computing platforms, optimizing memory footprint and computational efficiency.
Process sensor data, resolve 3D reconstruction challenges such as dynamic object interference and missing features, and build an automated data closed-loop from multimodal perception to ground-truth maps.

What we need to see:

Enrolled in a Bachelor’s degree or above, majoring in Computer Science, Robotics Engineering, Automation, Electronic Engineering, or related fields.
Proficient in C++/Python, with experience in at least one point cloud processing library (e.g., PCL, Open3D).
Solid understanding of 3D reconstruction, SfM, and point cloud registration algorithms; hands-on experience in 3D point cloud or semantic reconstruction projects.
Familiar with ROS1/ROS2 architectures; hands-on experience with physics simulation platforms (e.g., Isaac Sim) and multi-sensor calibration is highly preferred.
Experience with semantic SLAM, or NeRF/3DGS-based 3D reconstruction and scene representation is a strong plus.
Strong engineering problem-solving skills and the ability to quickly master new domain knowledge.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Participate in the R&D of multimodal perception and 3D reconstruction algorithms for embodied robots, focusing on 3D point cloud reconstruction and dynamic scene modeling.
Construct map representations tailored for embodied AI, and develop semantic-level 3D reconstruction algorithms (e.g., semantic point clouds, occupancy grids, 3D scene graphs).
Contribute to the engineering deployment of point cloud processing, semantic segmentation, and reconstruction algorithms on edge computing platforms, optimizing memory footprint and computational efficiency.
Process sensor data, resolve 3D reconstruction challenges such as dynamic object interference and missing features, and build an automated data closed-loop from multimodal perception to ground-truth maps.

What we need to see:

Enrolled in a Bachelor’s degree or above, majoring in Computer Science, Robotics Engineering, Automation, Electronic Engineering, or related fields.
Proficient in C++/Python, with experience in at least one point cloud processing library (e.g., PCL, Open3D).
Solid understanding of 3D reconstruction, SfM, and point cloud registration algorithms; hands-on experience in 3D point cloud or semantic reconstruction projects.
Familiar with ROS1/ROS2 architectures; hands-on experience with physics simulation platforms (e.g., Isaac Sim) and multi-sensor calibration is highly preferred.
Experience with semantic SLAM, or NeRF/3DGS-based 3D reconstruction and scene representation is a strong plus.
Strong engineering problem-solving skills and the ability to quickly master new domain knowledge.

Apply：please send your resume to hr@striding.ai

SLAM Algorithm Engineer

SLAM

Full-time/Internship

SLAMFull-time/Internship

What you'll be doing:

Participate in the design and development of multi-sensor fusion SLAM algorithms for mobile manipulators (wheel-arm robots) in indoor environments (e.g., narrow aisles, long corridors, dynamic areas).
Contribute to the development of localization features, optimizing front-end odometry and back-end state estimation against challenging conditions like chassis shaking and sensor occlusion to meet precision targets.
Analyze execution logs and sensor data to troubleshoot localization degradation or loss, enhancing overall system stability.

What we need to see:

Enrolled in a Bachelor’s degree or above, majoring in Computer Science, Robotics Engineering, Automation, Electronic Engineering, or related fields.
Proficient in C++/Python, with a solid mathematical foundation in multi-view geometry, state estimation, and non-linear optimization.
Familiar with at least one mainstream LiDAR SLAM or Visual/Visual-Inertial SLAM framework (e.g., FAST-LIO series, LOAM series, VINS, ORB-SLAM).
Familiar with ROS1/ROS2 architectures, with practical experience in spatial-temporal synchronization and joint intrinsic/extrinsic calibration of various sensors (LiDAR, Camera, IMU, Wheel Odometry).
Bonus: Development experience with mobile manipulators (wheel-arm robots), or understanding the impact of robotic arm kinematics on chassis state estimation is highly preferred.
Solid engineering implementation and troubleshooting capabilities.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Participate in the design and development of multi-sensor fusion SLAM algorithms for mobile manipulators (wheel-arm robots) in indoor environments (e.g., narrow aisles, long corridors, dynamic areas).
Contribute to the development of localization features, optimizing front-end odometry and back-end state estimation against challenging conditions like chassis shaking and sensor occlusion to meet precision targets.
Analyze execution logs and sensor data to troubleshoot localization degradation or loss, enhancing overall system stability.

What we need to see:

Enrolled in a Bachelor’s degree or above, majoring in Computer Science, Robotics Engineering, Automation, Electronic Engineering, or related fields.
Proficient in C++/Python, with a solid mathematical foundation in multi-view geometry, state estimation, and non-linear optimization.
Familiar with at least one mainstream LiDAR SLAM or Visual/Visual-Inertial SLAM framework (e.g., FAST-LIO series, LOAM series, VINS, ORB-SLAM).
Familiar with ROS1/ROS2 architectures, with practical experience in spatial-temporal synchronization and joint intrinsic/extrinsic calibration of various sensors (LiDAR, Camera, IMU, Wheel Odometry).
Bonus: Development experience with mobile manipulators (wheel-arm robots), or understanding the impact of robotic arm kinematics on chassis state estimation is highly preferred.
Solid engineering implementation and troubleshooting capabilities.

Apply：please send your resume to hr@striding.ai

Navigation & Planning Algorithm Engineer

Navigation

Full-time/Internship

NavigationFull-time/Internship

What you'll be doing:

Participate in the design of autonomous navigation and path planning algorithms, including global path planning, local trajectory generation, and dynamic obstacle avoidance strategies.
Develop and optimize navigation solutions based on ROS2 and the Nav2 framework, tailoring motion control to handle narrow passages, obstacles, and complex scenarios.
Contribute to the engineering deployment of navigation and planning algorithms on physical robot platforms, optimizing computing efficiency to meet real-time response demands.
Analyze test data to diagnose and resolve navigation anomalies (e.g., deadlocks, oscillations), continuously improving navigation success and arrival rates.

What we need to see:

Enrolled in a Bachelor’s degree or above, majoring in Computer Science, Robotics Engineering, Automation, Electronic Engineering, or related fields.
Proficient in C++/Python, with a strong grasp of data structures and core algorithms.
Familiar with the principles of common global and local path planning algorithms.
Familiar with the ROS1/ROS2 ecosystem and the configuration/custom development of the Nav2 framework; understanding of robot kinematic and dynamic models.
Bonus: Experience applying Reinforcement Learning to navigation, or hands-on experience with end-to-end navigation and obstacle avoidance.
Solid engineering implementation and troubleshooting capabilities.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Participate in the design of autonomous navigation and path planning algorithms, including global path planning, local trajectory generation, and dynamic obstacle avoidance strategies.
Develop and optimize navigation solutions based on ROS2 and the Nav2 framework, tailoring motion control to handle narrow passages, obstacles, and complex scenarios.
Contribute to the engineering deployment of navigation and planning algorithms on physical robot platforms, optimizing computing efficiency to meet real-time response demands.
Analyze test data to diagnose and resolve navigation anomalies (e.g., deadlocks, oscillations), continuously improving navigation success and arrival rates.

What we need to see:

Enrolled in a Bachelor’s degree or above, majoring in Computer Science, Robotics Engineering, Automation, Electronic Engineering, or related fields.
Proficient in C++/Python, with a strong grasp of data structures and core algorithms.
Familiar with the principles of common global and local path planning algorithms.
Familiar with the ROS1/ROS2 ecosystem and the configuration/custom development of the Nav2 framework; understanding of robot kinematic and dynamic models.
Bonus: Experience applying Reinforcement Learning to navigation, or hands-on experience with end-to-end navigation and obstacle avoidance.
Solid engineering implementation and troubleshooting capabilities.

Apply：please send your resume to hr@striding.ai

Chassis Control Engineer

Embedded

Full-time/Internship

EmbeddedFull-time/Internship

What you'll be doing:

Responsible for the hardware integration, assembly, and joint debugging of the robot chassis system, including component selection and adaptation for motors, ESCs, sensors, and control boards.
Design and implement low-level motion control algorithms to manage precise chassis movement execution.
Develop and maintain communication protocols and driver software between the low-level controllers and upper-level computing platforms.
Collaborate with the algorithm team for full-stack hardware-software co-debugging, resolving hardware and control issues such as wheel slippage, vibration, and response latency.

What we need to see:

Enrolled in an Associate degree or above, majoring in Automation, Mechatronics, Electronic Engineering, Mechanical Engineering, or related fields.
Proficient in C/C++, with solid experience in microcontroller/embedded system development (e.g., STM32, FreeRTOS).
Familiar with motor control principles and PID tuning; hands-on experience with common motors (brushless, stepper, servo), ESCs, and control boards.
Familiar with standard hardware communication interfaces and protocols (CAN, UART, SPI, I2C, etc.).
Understanding of ROS1/ROS2 low-level communication mechanisms (e.g., ros2_control); practical experience in physical hardware assembly is highly preferred.
Strong hands-on capabilities in hardware debugging and systematic troubleshooting.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Responsible for the hardware integration, assembly, and joint debugging of the robot chassis system, including component selection and adaptation for motors, ESCs, sensors, and control boards.
Design and implement low-level motion control algorithms to manage precise chassis movement execution.
Develop and maintain communication protocols and driver software between the low-level controllers and upper-level computing platforms.
Collaborate with the algorithm team for full-stack hardware-software co-debugging, resolving hardware and control issues such as wheel slippage, vibration, and response latency.

What we need to see:

Enrolled in an Associate degree or above, majoring in Automation, Mechatronics, Electronic Engineering, Mechanical Engineering, or related fields.
Proficient in C/C++, with solid experience in microcontroller/embedded system development (e.g., STM32, FreeRTOS).
Familiar with motor control principles and PID tuning; hands-on experience with common motors (brushless, stepper, servo), ESCs, and control boards.
Familiar with standard hardware communication interfaces and protocols (CAN, UART, SPI, I2C, etc.).
Understanding of ROS1/ROS2 low-level communication mechanisms (e.g., ros2_control); practical experience in physical hardware assembly is highly preferred.
Strong hands-on capabilities in hardware debugging and systematic troubleshooting.

Apply：please send your resume to hr@striding.ai

Robotics Electronics Hardware Engineer

Hardware

Full-time

HardwareFull-time

What you'll be doing:

Design the hardware system architecture for robotic systems, including system block diagrams, interface definitions, power consumption analysis, and key component selection strategies;
Lead and collaborate on system-level development of the robot's hardware technology stack: evaluate and develop solutions for computing platforms (MCU/SoC/x86/ARM), main control boards, and carrier boards (computing unit controllers);
Establish and optimize hardware development processes: requirements gathering and decomposition, hardware design, schematic/PCB review, design validation, reliability and environmental testing;
Analyze and resolve critical hardware issues such as EMI/EMC, ESD, thermal failure, power supply stability, signal integrity, and failures caused by vibration and shock;
Collaborate closely with mechanical, embedded software, algorithm, system testing, supply chain, and manufacturing teams to drive robot system iteration and mass production ramp-up;
Participate in supplier technical evaluation and component lifecycle management to ensure stable delivery and quality;
Document and standardize hardware design specifications, test procedures, failure case databases, and platform-based reusable solutions.

What we need to see:

More than 3 years of experience in hardware-related fields;
Proficient in using Cadence design software, skilled in embedded hardware development with strong circuit design and debugging capabilities, familiar with common sensors and interfaces;
Familiar with development and debugging of core components such as CPUs and MCUs, with experience in product environmental testing, safety regulations, and EMC testing;
Familiar with hardware development processes and the work of upstream and downstream departments, with experience in driving trial production and mass production, as well as DFX development;
Strong ability in problem analysis and resolution;
Clear logical thinking, extensive experience in prototype debugging, and a mature methodology for solving problems;
Capable of handling work pressure, highly responsible, with excellent communication skills and a strong team spirit.

Apply：please send your resume to hr@striding.ai

What you'll be doing:

Design the hardware system architecture for robotic systems, including system block diagrams, interface definitions, power consumption analysis, and key component selection strategies;
Lead and collaborate on system-level development of the robot's hardware technology stack: evaluate and develop solutions for computing platforms (MCU/SoC/x86/ARM), main control boards, and carrier boards (computing unit controllers);
Establish and optimize hardware development processes: requirements gathering and decomposition, hardware design, schematic/PCB review, design validation, reliability and environmental testing;
Analyze and resolve critical hardware issues such as EMI/EMC, ESD, thermal failure, power supply stability, signal integrity, and failures caused by vibration and shock;
Collaborate closely with mechanical, embedded software, algorithm, system testing, supply chain, and manufacturing teams to drive robot system iteration and mass production ramp-up;
Participate in supplier technical evaluation and component lifecycle management to ensure stable delivery and quality;
Document and standardize hardware design specifications, test procedures, failure case databases, and platform-based reusable solutions.

What we need to see:

More than 3 years of experience in hardware-related fields;
Proficient in using Cadence design software, skilled in embedded hardware development with strong circuit design and debugging capabilities, familiar with common sensors and interfaces;
Familiar with development and debugging of core components such as CPUs and MCUs, with experience in product environmental testing, safety regulations, and EMC testing;
Familiar with hardware development processes and the work of upstream and downstream departments, with experience in driving trial production and mass production, as well as DFX development;
Strong ability in problem analysis and resolution;
Clear logical thinking, extensive experience in prototype debugging, and a mature methodology for solving problems;
Capable of handling work pressure, highly responsible, with excellent communication skills and a strong team spirit.

Apply：please send your resume to hr@striding.ai