Member of Technical Staff - Simulation, Frontier AI Robotics

Amazon
San Francisco, California, USA2026-05-22ONSITE

About the job

We are seeking a Member of Technical Staff Simulation Engineer to join our AI robotics research team developing foundation models for robotics. You will rapidly develop 3D physics-based and photorealistic simulations alongside scientists to enable training large-scale machine learning models.

Responsibilities

- Develop simulations for reinforcement learning, closed-loop simulations and synthetic data generation

- Implement essential robotics features, including accurate modeling of sensors, actuators, and controllers

- Build real-to-sim workflows for dynamic environments and robotics tasks

- Implement simulation features to minimize sim-to-real gaps through domain randomization and system identification

- Create asset toolchains supporting industry-standard formats (URDF, MJCF, USD)

- Collaborate closely with a team of ML researchers to enable large-scale robotics training pipelines

Qualifications

Minimum

- 5+ years of non-internship professional software development experience

- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience

- 5+ years of programming with at least one software programming language experience

- Experience as a mentor, tech lead or leading an engineering team

- Bachelor's degree in computer science or equivalent

- Experience leading engineering teams as a mentor or tech lead

- Expertise in Python, C++ and CUDA programming

- Experience with TensorRT or similar ML optimization frameworks

- Ability to optimize ML models for production

Preferred

- Expertise in NVIDIA's ML stack (cuDNN, CUDA Graph, etc.)

- Experience with ML compilers (ONNX Runtime, TVM, etc.)

- Experience with transformer model optimization

- Background in performance profiling and optimization

- Experience working directly with research teams

- Ability to build robust monitoring systems

- Experience with large-scale ML serving systems