Position: Senior Robotics Data Engineer
Location: Warren, Michigan
Duration: 12+Months with possible extensions
Main Skills: Senior Robotics Data Engineer (ML/AI systems, Python, TensorFlow and/or PyTorch, Power BI, Azure data services)
Position Overview:
Client is seeking a Senior Robotics Data Engineer to join the Autonomous Robotics Center (ARC) Advanced Development team. This role is pivotal to a large-scale robotics initiative, enabling scalable AI perception and grasping solutions across thousands of manufacturing parts. The successful candidate will architect and manage end-to-end robotic data systems, including real-world capture, simulation-generated data, annotation, curation, and lifecycle management. Your contributions will directly support AI models for perception, grasping, and manipulation, facilitating rapid scaling across diverse manufacturing contexts.
Key Responsibilities:
- 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 minimize manual annotation costs.
- Enable simulation-to-real (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 Robotics Perception, Grasping AI, and Simulation teams to define data requirements and KPIs.
- Establish data quality metrics that correlate with perception and grasping performance.
Required Qualifications:
- Minimum 3 years of experience in data engineering, machine learning systems, robotics, or related fields.
- Master's degree in Engineering, Computer Science, Data Science, or equivalent practical experience.
- Proven experience building production-grade data pipelines for ML/AI systems.
- Strong hands-on experience with Python-based data tooling.
- Experience working with large, complex, multimodal datasets.
- Systems thinking mindset and strong cross-functional collaboration skills.
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