Principal Applied Scientist Perception, Compass

Amazon • Pasadena, CA

11d • $198,900 - $269,000 • Onsite

About The Position

We are seeking a Principle Applied Scientist to join Compass. In this role, you will own the perception input into the Compass safety system, defining how robots perceive, interpret, and anticipate their surroundings in safety-critical contexts. You will develop novel approaches to environment understanding that go beyond static scene representation, providing real-time, predictive models of how humans, objects, and dynamic obstacles may evolve over short time horizons. Your work will directly unlock robot performance by replacing conservative assumptions with precise, learned understandings of risk. You will set the scientific direction for perception within Compass, collaborate closely with controls, planning, and firmware teams, and influence the broader Amazon Robotics safety architecture.

Requirements

• 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 Haves

• 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

Responsibilities

• Define and drive the long-term scientific vision for safety-critical perception within Compass, spanning multiple robot platforms and deployment environments
• Develop novel perception algorithms that provide real-time, predictive representations of dynamic environments including human motion forecasting, obstacle trajectory prediction, and scene evolution modeling
• Design perception outputs that are tightly coupled to safety constraints, enabling control barrier functions to operate with minimal conservatism while maintaining formal safety guarantees
• Research and develop methods to quantify and bound perception uncertainty, providing calibrated confidence estimates that safety systems can reason over
• Architect perception pipelines that generalize across sensor modalities (LiDAR, depth cameras, RGB, radar) and robot morphologies without platform-specific retraining
• Investigate the application of foundation models and large-scale pre-training to safety-critical perception tasks, establishing when and how learned representations can be trusted at safety-critical confidence levels
• Collaborate with controls, motion planning, and firmware teams to define interface contracts between perception and downstream safety modules
• Publish research at top-tier venues and represent Amazon Robotics in the broader academic and industry community
• Mentor and develop a team of applied scientists and research engineers
• Influence Amazon Robotics' safety architecture and perception strategy at the organizational level

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: Ph.D. or professional degree