AI Research and Development Engineer (physical Ai)

Intel Intel · Semiconductors · Netherlands · Remote

This role focuses on building the end-to-end stack for robotic intelligence, bridging high-level research and low-level performance optimization for physical AI. It involves training large-scale Vision-Language-Action (VLA) models and optimizing them for real-time deployment on edge hardware, including developing safety runtimes and model-agnostic inference APIs.

What you'd actually do

  1. Implement and fine-tune state-of-the-art Vision-Language-Action policies for robotic manipulation and control.
  2. Export and optimize VLA models for edge deployment without accuracy loss via export pipelines, graph optimization, and precision calibration to ensure policies run at high frequencies on constrained hardware.
  3. Develop safety runtimes to manage action clamping, velocity limits, and workspace bounds for reliable real-world operation.
  4. Build and maintain model-agnostic inference APIs that abstract across different robotic platforms and hardware backends.
  5. Support deployment across a range of edge accelerators and inference runtimes to ensure broad hardware compatibility.

Skills

Required

  • PyTorch
  • PyTorch Lightning
  • distributed training
  • robotic systems (industrial robots, EMR, humanoid robots)
  • robotics hardware and software stacks (cameras, depth sensors, robot control interfaces, ROS)
  • real-world robot deployment and integration
  • Vision-Language-Action (VLA) architectures
  • imitation learning
  • embodied agents
  • model export and optimization (graph compilation, operator fusion, precision calibration)
  • inference runtime backends (OpenVINO, ONNX Runtime, ExecuTorch)
  • Python
  • deployment dependencies management

Nice to have

  • PhD in robotics, machine learning, computer vision, or related field
  • open-source robotics contributions
  • open-source embodied AI contributions
  • open-source model optimization contributions
  • low-level performance profiling
  • memory optimization
  • hardware-aware optimization for edge and robotic devices

What the JD emphasized

  • entire lifecycle of physical AI
  • entirely open-source
  • end-to-end stack for robotic intelligence
  • intersection of high-level research and low-level performance optimization
  • run reliably on edge hardware with minimal latency
  • export and optimize VLA models for edge deployment
  • ensure policies run at high frequencies on constrained hardware
  • model-agnostic inference APIs
  • range of edge accelerators and inference runtimes
  • extensive experience with PyTorch and PyTorch Lightning, including distributed training for large-scale models
  • hands-on experience working with robotic systems
  • strong familiarity with robotics hardware and software stacks
  • deep understanding of Vision-Language-Action (VLA) architectures
  • technical expertise in model export and optimization
  • familiarity with inference runtime backends
  • write clean, modular Python code
  • manage complex deployment dependencies
  • own the full pipeline, from training a model to watching it control a physical arm
  • prioritize performance and real-world utility over theoretical cloud metrics

Other signals

  • end-to-end stack for robotic intelligence
  • training large-scale Vision-Language-Action (VLA) models
  • lightweight runtimes for real-time deployment
  • low-level performance optimization
  • run reliably on edge hardware with minimal latency