Physical AI Engineer (robotics Ad & Optimization)

AMD AMD · Semiconductors · Stockholm, Sweden · Engineering

AMD is seeking a Physical AI Engineer to build and ship production AI models and training pipelines for robotics and autonomous systems. The role involves designing, training, and scaling multimodal foundation models, including VLMs, world models, and VLAs. The engineer will also contribute to open-source codebases and advance the state of the art through publications. Collaboration with AI Engineering, Product, and Software Engineering teams is key, as is integrating AI workloads into products and supporting CI/CD validation. The first 6 months involve becoming proficient with the codebase, assessing developments, training and releasing an end-to-end model, and leading client-facing implementation efforts.

What you'd actually do

  1. Build and ship production AI models and training pipelines for robotics and autonomous systems.
  2. Design, train, and scale large multimodal foundation models, including VLMs, world models, vision-language-action models (VLAs), 3D scene reconstruction (e.g., 3D Gaussian Splatting), perception systems, and data curation frameworks.
  3. Develop and contribute to open-source codebases, tooling, and reference implementations to accelerate adoption and collaboration.
  4. Advance the state of the art through publications and open model/code releases.
  5. Lead client-facing implementation efforts to identify product needs and convert them into actionable technical deliverables.

Skills

Required

  • Master's degree, PhD, or equivalent experience in Machine Learning, Robotics, or a related field, including 3+ years of relevant industry experience.
  • Experience building robotics, perception, or autonomous systems pipelines.
  • Strong foundation in deep learning for perception and embodied decision-making, including transformers, diffusion models, and world models.
  • Hands-on experience with vision and multimodal foundation models (e.g., ViT, CLIP, DINO, LLaVA) and VLAs (e.g., OpenVLA, Pi-0.5).
  • Experience using simulation environments and RL/IL techniques to train and evaluate embodied agents (e.g., Isaac Lab, MuJoCo, Genesis, LeRobot).
  • Proficiency in Python and familiarity with C++ in production environments.
  • Strong experience with PyTorch
  • Strong engineering skills, including rapid prototyping, debugging, profiling, optimization, AI-assisted development tools (e.g., Claude Code, Cursor), and delivering maintainable production code.

Nice to have

  • Experience with JAX
  • Experience building and operating large-scale machine learning systems, including training infrastructure and distributed computing environments.
  • Publication record in leading conferences such as CVPR, ICCV, ECCV, NeurIPS, ICRA, or IROS.
  • Experience profiling and optimizing GPU workloads using ROCm and/or CUDA.
  • Experience developing, maintaining, and supporting open-source software projects, including releases, documentation, and CI pipelines.
  • Experience with cloud platforms (AWS, GCP, Azure) and cluster orchestration technologies such as Slurm, Kubernetes, or Yarn.

What the JD emphasized

  • production AI models
  • train and release an end-to-end model
  • integrate AI workloads into products

Other signals

  • build and ship production AI models
  • train and release an end-to-end model
  • integrate AI workloads into products