Member of Technical Staff, Microsoft Robotics (spatial Ai)

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

The Member of Technical Staff, Microsoft Robotics (Spatial AI) role focuses on designing, developing, and testing physical world models for robots to understand, predict, and reason about 3D physical environments. This involves building models for spatial structure, object relationships, physics dynamics, and scene semantics, enabling robots with physical intuition for manipulation, navigation, and interaction planning. The role contributes to world modeling, spatial AI, and foundation models for robotics, predicting physical world changes in response to robot actions. Responsibilities include designing and evaluating world models, building training data pipelines, and collaborating with researchers and engineers.

What you'd actually do

  1. Design, develop, and evaluate physical world models that capture 3D spatial structure, object geometry and pose, physics dynamics, material properties, and semantic scene understanding for robotic applications.
  2. Build and train world models (e.g., video prediction models, neural physics simulators, 3D generative models, scene graph representations) that predict future states of physical environments conditioned on robot actions, enabling model-based planning and policy learning.
  3. Develop spatial AI capabilities including 3D scene reconstruction, object detection and pose estimation, spatial relationship reasoning, occupancy prediction, and dense 3D feature representations for robot perception and planning.
  4. Implement and maintain evaluation frameworks for world models and spatial AI systems, including prediction accuracy metrics, planning performance benchmarks, and generalization testing across environments and object categories.
  5. Collaborate with robotics researchers, learning engineers, and simulation engineers to integrate world models into robot planning and control pipelines, enabling model-predictive control, imagination-based planning, and data-augmented training.

Skills

Required

  • Python
  • PyTorch
  • JAX
  • TensorFlow
  • 3D scene reconstruction
  • object detection
  • pose estimation
  • spatial relationship reasoning
  • occupancy prediction
  • dense 3D feature representations
  • multi-sensor data fusion
  • RGB
  • depth
  • LiDAR
  • proprioception
  • scene annotation
  • dataset curation
  • model-based planning
  • policy learning
  • model-predictive control
  • imagination-based planning
  • data-augmented training
  • video prediction models
  • neural physics simulators
  • 3D generative models
  • scene graph representations
  • robot perception
  • robot planning
  • robot control
  • robot manipulation
  • robot navigation
  • robot interaction planning
  • Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience

Nice to have

  • robotics perception
  • robotics navigation
  • proprioceptive sensor stacks
  • systems integration
  • algorithm development
  • model development
  • implementation in real-world systems

What the JD emphasized

  • physical intelligence
  • world modeling
  • spatial AI
  • foundation models for robotics
  • physical intuition
  • robotics AI models
  • physically grounded agentic AI workflows
  • trustworthy test and evaluation
  • real-world customer-focused validation
  • physical world
  • robotics
  • physical understanding

Other signals

  • physical intelligence platform
  • world modeling
  • spatial AI
  • foundation models for robotics
  • robotics AI models
  • physically grounded agentic AI workflows