Applied Scientist, Navigation

Amazon Amazon · Big Tech · San Francisco, CA · Applied Science

This role focuses on designing, developing, and deploying advanced navigation systems for robotic systems, leveraging cutting-edge AI, foundation models, and control-theoretic approaches. The scientist will lead research, own ML models end-to-end, and translate research into deployed production systems for autonomous robotics at scale.

What you'd actually do

  1. Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding
  2. Lead research initiatives in computer vision, sensor fusion and 3D perception
  3. Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities
  4. Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment
  5. Mentor junior scientists and engineers; contribute to a culture of technical excellence

Skills

Required

  • Java
  • C++
  • Python
  • Robot navigation
  • Motion planning
  • Autonomous systems
  • Learning-based approaches to navigation
  • Model Predictive Control (MPC)
  • Optimization-based planning
  • PyTorch
  • JAX

Nice to have

  • Foundation models
  • Large pre-trained models
  • Embodied AI
  • World models
  • Visual navigation
  • Vision-language action models
  • Sim-to-real transfer
  • High-fidelity simulation environments
  • Isaac Sim
  • MuJoCo
  • Gazebo
  • SLAM
  • Localization
  • Mapping systems
  • ROS/ROS2
  • Real-time robotics middleware
  • Physical robots in dynamic, real-world environments
  • Safety-critical systems
  • Formal verification of learned controllers
  • Multi-agent coordination
  • Fleet-level navigation

What the JD emphasized

  • Proven track record of translating research into deployed systems
  • Experience applying foundation models or large pre-trained models to robotics tasks
  • Hands-on experience deploying navigation systems on physical robots in dynamic, real-world environments
  • Experience with safety-critical systems and formal verification of learned controllers

Other signals

  • foundation models
  • embodied agents
  • learning-based planning and control
  • robot navigation
  • deployed systems