Principal Applied Scientist Perception, Compass

Principal Applied Scientist Perception, Compass

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.

• 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.

Work with the inventors of control barrier functions on a novel, universal approach to safe autonomy. You’ll push the boundary of safe performance by integrating safety with motion planning, RL, and foundation models, ensuring that safety is never a blocker to robot performance.

• 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.

• 10+ years of relevant work in industry or academia.
• Experience creating novel algorithms and advancing the state of the art.
• 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…