Senior Robotics Data Engineer

We are seeking a Senior Robotics Data Engineer to join the Autonomous Robotics Center (ARC) Advanced Development
team. This role is foundational to GM s large-scale robotics initiative, enabling generalized and scalable AI perception
and grasping solutions across thousands of manufacturing parts.
You will architect and work on end-to-end robotic data systems, spanning real-world capture, simulation-generated
data, annotation, curation, and lifecycle management. Your work will directly power perception, grasping, and
manipulation AI models, enabling rapid scaling across diverse part geometries and manufacturing contexts.
What You Will Do
Design and implement scalable data pipelines for large-scale robotic datasets (vision, depth, tactile,
force/torque).
Build infrastructure to support high-throughput data capture from real robots and simulation environments.
Develop and deploy semi-supervised / self-supervised data labeling workflows to reduce manual annotation
cost.
Enable simulation-to-real (Sim2Real) data workflows, including domain randomization and synthetic data
generation.
Own data versioning, metadata, and dataset governance to support model training, evaluation, and regression
testing.
Partner closely with Robotics Perception, Grasping AI, and Simulation teams to define data requirements and
KPIs.
Establish data quality metrics that directly correlate with perception and grasping performance.
Required Qualifications
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 with strong cross-functional collaboration skills.
Preferred Qualifications (Competitive Edge)
Direct experience supporting robotics perception, grasping, or manipulation AI.
Familiarity with robotics simulation platforms such as 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 (e.g., Power BI, Azure data services).
Exposure to self-supervised or weakly supervised learning techniques