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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