Member of Technical Staff, Microsoft Robotics (robotics Data)

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Data Science

This role focuses on data analysis, curation, and visualization for robotics AI models. It involves defining data collection strategies, building data pipelines, analyzing dataset characteristics, and creating visualization tools to improve model performance and identify data gaps. The role works at the intersection of robotics, machine learning, and data engineering.

What you'd actually do

  1. Define and implement data collection strategies for robot learning, including specifying demonstration coverage requirements, environmental diversity targets, task distribution plans, and quality acceptance criteria for teleoperation, egocentric, and autonomous data collection campaigns.
  2. Build and maintain data curation pipelines that ingest, clean, validate, label, and version robotics datasets (manipulation demonstrations, navigation trajectories, sensor logs, simulation rollouts), ensuring data integrity and provenance tracking.
  3. Develop data analysis frameworks that quantify dataset characteristics (coverage, diversity, balance, quality scores), identify data gaps and biases, and provide recommendations for targeted data collection to improve model performance.
  4. Create interactive data visualization tools and dashboards (using tools such as Power BI, Plotly, or custom web applications) that enable researchers, engineers, and leadership to explore dataset properties, model training metrics, evaluation results, and fleet operational telemetry.
  5. Collaborate with ML researchers and learning engineers to design and execute experiments that measure the impact of data quantity, quality, and diversity on model performance, producing statistical analyses that guide data investment decisions.

Skills

Required

  • Python (Pandas, NumPy, scikit-learn, matplotlib)
  • Data analysis
  • Data curation
  • Data visualization
  • Statistical techniques (hypothesis testing, causal inference, regression analysis, clustering)
  • Data collection strategies
  • Data integrity
  • Provenance tracking
  • Ethics and privacy policies related to data
  • Bias detection
  • Consent
  • Data governance

Nice to have

  • Power BI
  • Plotly
  • Custom web applications
  • Robotics data experience

What the JD emphasized

  • robotics data

Other signals

  • robot learning
  • data curation
  • data analysis
  • model performance