Principal Applied Scientist Perception, Compass

Amazon Amazon · Big Tech · Pasadena, CA · Applied Science

Seeking a Principal Applied Scientist to lead safety-critical perception for robots, developing novel real-time predictive models of dynamic environments and human motion. This role involves architecting generalizable perception pipelines across sensor modalities, investigating foundation models, and quantifying perception uncertainty to ensure safe robot autonomy.

What you'd actually do

  1. Define and drive the long-term scientific vision for safety-critical perception within Compass, spanning multiple robot platforms and deployment environments
  2. Develop novel perception algorithms that provide real-time, predictive representations of dynamic environments including human motion forecasting, obstacle trajectory prediction, and scene evolution modeling
  3. Design perception outputs that are tightly coupled to safety constraints, enabling control barrier functions to operate with minimal conservatism while maintaining formal safety guarantees
  4. Research and develop methods to quantify and bound perception uncertainty, providing calibrated confidence estimates that safety systems can reason over
  5. Architect perception pipelines that generalize across sensor modalities (LiDAR, depth cameras, RGB, radar) and robot morphologies without platform-specific retraining

Skills

Required

  • PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field
  • 6+ years of experience in perception research and development, with a significant portion in robotics or embodied AI
  • Deep expertise in one or more of: 3D scene understanding, object detection and tracking, motion prediction, occupancy forecasting, or semantic scene representation
  • Proven track record of publications at top-tier venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICRA, RSS, CoRL)
  • Experience deploying perception systems on physical robots operating in unstructured or human-shared environments
  • Proficiency in Python and C++ with experience writing production-grade perception code
  • Demonstrated ability to set technical direction and influence across teams at a senior level

Nice to have

  • 10+ years of relevant work in industry or academia experience
  • Experience creating novel algorithms and advancing the state of the art
  • Have peer-reviewed scientific contributions in premier journals and conferences
  • Experience with safety-critical perception, including uncertainty quantification, out-of-distribution detection, or formal verification of learned perception models
  • Familiarity with control barrier functions, reachability analysis, or other formal safety methods and how perception feeds into them
  • Experience with real-time perception on resource-constrained hardware (edge compute, embedded GPUs)
  • Track record of building perception systems that generalize across multiple sensor configurations or robot platforms
  • Experience with foundation models or large-scale self-supervised learning applied to robotics perception
  • Knowledge of functional safety standards (e.g., IEC 61508, ISO 13849) as they relate to perception system design
  • Experience with human motion prediction, intent estimation, or social navigation
  • Demonstrated ability to build and lead a research team, including hiring, mentoring, and career development
  • Strong cross-functional collaboration skills with experience influencing product and architecture decisions at the organizational level

What the JD emphasized

  • safety-critical contexts
  • real-time, predictive models
  • safety-critical perception
  • safety-critical confidence levels
  • safety-critical perception

Other signals

  • Develop novel perception algorithms
  • real-time, predictive models
  • foundation models and large-scale pre-training