Fellow Engineer: Physical AI & Robotics Simulation (Hybrid)

Advanced Micro Devices, Inc.; San Jose (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Advanced Micro Devices, Inc. in San Jose, CA is seeking a senior Fellow to advance simulation and training foundations for Physical AI.

You will help design GPU-accelerated pipelines for simulation, data generation, and model policy training at scale. As a senior member, you will push the boundaries of embodied AI, connect high-throughput physics with VLA/WAM models, and influence hardware-software co-design while publishing results at major venues.

Qualifications

• PhD or master's degree in CS, Robotics, EE, Mathematics, or related field.
• Hands-on experience with GPU-accelerated robotics simulation and large-scale training.
• Publications in top conferences (e.g., NeurIPS, ICML, ICLR).
• Strong software development and collaboration across teams.

Responsibilities

• Research and implement novel VLA/WAM architectures for Physical AI models and demonstrate their benefits on AMD platforms.
• Demonstrate results through working proof-of-concepts and contribute your work to the open-source community.
• Increase adoption of agentic workflows for optimizing and deploying Physical AI at scale on AMD platforms.
• Influence hardware-software co-design through quantitative analysis to guide key decisions for future AMD platforms.
• Publish and promote your work at external venues, including major conferences.
• Collaborate with researchers within AMD and across industry and academia to promote innovation on AMD platforms.

Skills

Robotics simulation

RL training

VLA/WAM architectures

GPU performance analysis

Open source contributions

Publications

Education

PhD or Master’s degree

Tools

Isaac Sim

MuJoCo

Warp

Newton

Job description

Advanced Micro Devices, Inc. in San Jose, CA is seeking a senior Fellow to advance simulation and training foundations for Physical AI.

You will help design GPU-accelerated pipelines for simulation, data generation, and model policy training at scale. As a senior member, you will push the boundaries of embodied AI, connect high-throughput physics with VLA/WAM models, and influence hardware-software co-design while publishing results at major venues.