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Robotics Data Engineer

Job in Warren, Macomb County, Michigan, 48088, USA
Listing for: Jconnect Infotech Inc
Full Time, Seasonal/Temporary position
Listed on 2026-07-01
Job specializations:
  • IT/Tech
    Data Engineering, Robotics, AI Engineer (Applied/Software), Machine Learning/ ML Engineer
  • Engineering
    Data Engineering, Robotics, AI Engineer (Applied/Software)
Job Description & How to Apply Below

Robotics Data Engineer

Title:

Robotics Data Engineer

Location:

Warren, MI/ Mountain View, CA

Duration:
Fulltime

Job Description:

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 Tensor Flow 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 (Sim2

    Real) 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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