Must Have Technical/Functional Skills
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.
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.
Roles & Responsibilities
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
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