Senior Robotics Data Engineer / Senior Robotics Data Collection Engineer

We're Hiring — 2 Openings Robotics AI Team | Advanced Manufacturing Automation

Location: Warren, MI — Full-time Onsite, 5 days/week Work Authorization: Visa independent. Must be on our W2 — no C2C or 1099. Residency: Local candidates preferred. Open to candidates willing to relocate to Warren, MI. Contract Type: W2 Contract Duration: 12 months Start: ASAP Level: Senior Travel: None

Interview Process: Multiple rounds, quick turnaround. In-person interview may be required.

About the Team These roles sit inside an Autonomous Robotics Center AI team building scalable perception and grasping solutions across thousands of manufacturing parts. The work directly powers the next generation of intelligent manufacturing automation — real production impact, not internal tooling.

Role 1 of 2 — Senior Robotics Data Engineer

What You'll Be Doing

• Design and implement scalable data pipelines for large-scale robotic datasets — vision, depth, tactile, force/torque.
• Build infrastructure for high-throughput data capture from real robots and simulation environments.
• Develop and deploy semi-supervised/self-supervised data labeling workflows to cut manual annotation costs.
• Enable Sim2Real data workflows, including domain randomization and synthetic data generation.
• Manage data versioning, metadata, and dataset governance to support model training, evaluation, and regression testing.
• Collaborate with Perception, Grasping AI, and Simulation teams to define data requirements and KPIs.
• Establish data quality metrics that tie directly to perception and grasping model performance.

Required Qualifications

• 3 years in data engineering, ML systems, robotics, or a related field.
• Master's degree in Engineering, Computer Science, Data Science, or equivalent hands-on experience.
• Proven track record building production-grade data pipelines for ML/AI systems.
• Strong Python-based data tooling experience.
• Experience working with large, complex, multimodal datasets.
• Systems thinker with solid cross-functional collaboration skills.

Preferred Qualifications

• Direct experience supporting robotics perception, grasping, or manipulation AI.
• Familiarity with robotics simulation platforms (e.g., Isaac Sim) and synthetic data generation.
• Experience with data labeling tools and annotation workflows at scale.
• Hands-on knowledge of TensorFlow and/or PyTorch from a data systems perspective.
• Experience with Microsoft data ecosystems — Power BI, Azure data services.
• Exposure to self-supervised or weakly supervised learning techniques.

Role 2 of 2 — Senior Robotics Data Collection Engineer

What You'll Be Doing

• Collect high-quality robot telemetry, sensor, and visual data from manufacturing robotic systems in lab and production-like environments.
• Operate and monitor robotic systems, GELLO teleop interfaces, and data collection hardware.
• Organize, label, and validate data per established annotation guidelines and quality standards.
• Perform manual annotation and verification to generate accurate ground truth labels.
• Run data collection campaigns following documented protocols and experimental designs.
• Troubleshoot data collection issues and document problems clearly for engineering teams.
• Work directly with AI engineers, robotics engineers, and manufacturing teams to ensure data meets model training requirements.

Required Qualifications

• BS in Mechanical Engineering, Electrical Engineering, or a related field.
• Strong, demonstrated hands-on experience operating, troubleshooting, and maintaining industrial or collaborative robotic arms.
• Proficiency in Linux environments and basic Python scripting to interface with robotic systems and manage data pipelines.
• Proven experience with perception sensors and capturing/validating high-quality sensor data.
• Detail-oriented, reliable, and comfortable working independently or as part of a team.
• Ability to follow technical procedures and documentation precisely.

Preferred Qualifications

• Experience in automotive or manufacturing environments.
• Familiarity with machine learning, AI, or data annotation concepts.
• Background in robotics, manufacturing, or structured data collection.
• Experience operating or troubleshooting technical equipment in production-like settings.