Fellow Engineer, Physical AI

ADVANCE YOUR CAREER. ADVANCE THE WORLD.  At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.    Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career.

The Efficient AI Models and Applications team at AMD is looking for a specialized Fellow who is passionate about simulation and training foundations for Physical AI. You will be a key member of the core team innovating efficient end-to-end GPU-accelerated Physical AI pipelines for simulation, synthetic data generation, and model policy training at scale.   THE PERSON: The ideal candidate has hands-on experience working on embodied AI and robot learning and can bridge the gap between high-throughput physics simulation and training Vision Language Action (VLA) Models / World Action Models (WAMs). They are passionate about enabling and innovating efficient approaches on AMD GPUs.   Why Join Us? Exciting Opportunities: As a senior member of the team, you will be at the forefront of innovation, working with the latest Physical AI models and algorithms. You will have the opportunity to shape the future of AI model training and inference optimizations across a variety of applications. Talented Team: Join a team of highly skilled industry specialists who are passionate about pushing the boundaries of AI. Collaborate with like-minded professionals and learn from the best in the field. Impactful Work: Your contributions will directly influence how cutting-edge foundation models for the Physical AI domain are efficiently trained and deployed at scale across the industry, making a significant difference in several industries and applications.

Research and implement novel, efficient VLA/WAM architectures for Physical AI models and showcase 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 across numerical and hardware design trade-offs for future-generation 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.

Hands-on experience with GPU-accelerated robotics simulation and large-scale parallel-environment training: Isaac Sim/Isaac Lab, MuJoCo/MJX, Warp or Newton, Genesis. Demonstrated results training robot policies at scale, including reinforcement learning, imitation learning, or VLA and diffusion-policy training, with real sim-to-real transfer experience. GPU performance-analysis skills and experience optimizing workloads for simulation and RL training are preferred. Publications in conferences such as NeurIPS, CoRL, RSS, ICRA, IROS, CVPR, ICML, ICLR, etc. Strong technical expertise in algorithmic innovation for efficient simulation, training, and inference. Excellent written, verbal, and presentation skills, and the ability to zoom out and identify key trends in the industry. Several years of experience in robotics, GenAI, and related software development.   ACADEMIC CREDENTIALS: PhD or master's degree or higher in CS, Robotics, EE, Mathematics, or a related field.   LOCATION: San Jose, CA (Hybrid) Seattle may also be considered.   #LI-MV1 #LI-HYBRID   Benefits offered are described:  AMD benefits at a glance .   AMD does not accept unsolicited resumes from