Applied Scientist II

Amazon Amazon · Big Tech · N.reading, MA · Applied Science

This role focuses on developing advanced robotics systems by advancing physics-based simulation, sim-to-real transfer, and machine learning approaches. The primary goal is to create adaptable automation solutions for complex environments, with a secondary focus on data engineering for simulation.

What you'd actually do

  1. Advance physics-based simulation fidelity for contact-rich manipulation and locomotion
  2. Design and build high-performance simulation tools integrated into a robotics design stack
  3. Translate research ideas into robust, verifiable data
  4. Develop methods to quantify and reduce simulation-to-reality gaps across design, safety, and control
  5. Architect scalable simulation solutions for rigid and deformable body dynamics

Skills

Required

  • Java, C++, Python or related language
  • PhD in computer science, computer engineering, or related field
  • 2+ years of science, technology, engineering or related field experience
  • Deep expertise in physics-based simulation, including rigid and deformable dynamics, contact mechanics, computational geometry, and numerical methods
  • Experience designing and optimizing physics-based simulation systems for high-performance and large-scale computing environments
  • maintainable, performance-critical code
  • Working knowledge of modern physics engines such as MuJoCo, Isaac Lab, Drake, and Newton

Nice to have

  • reinforcement learning and policy training in simulation
  • differentiable physics, learned simulation models, or neural physics engines
  • contact-rich manipulation or legged locomotion simulation
  • robotics model formats and pipelines (e.g., URDF, SDF, USD)
  • GPU-accelerated computing and algorithms
  • deploying simulation-trained policies on real robotic systems
  • Demonstrated research leadership, from project conception through publication and deployment

What the JD emphasized

  • PhD in computer science, computer engineering, or related field
  • Deep expertise in physics-based simulation
  • Experience designing and optimizing physics-based simulation systems for high-performance and large-scale computing environments
  • maintainable, performance-critical code
  • Working knowledge of modern physics engines such as MuJoCo, Isaac Lab, Drake, and Newton
  • Demonstrated research leadership, from project conception through publication and deployment

Other signals

  • develop advanced robotics systems
  • cutting-edge AI
  • adaptable automation solutions
  • robotic dexterous manipulation, locomotion, and human-robot interaction
  • develop and apply cutting-edge simulation methodologies for advanced robotics systems
  • physics-based simulation techniques
  • sim-to-real transfer methods
  • machine learning approaches
  • robotics design stack
  • digital twin level of fidelity
  • reinforcement learning
  • hardware-in-the-loop (HIL)
  • sim-to-real transfer