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Head of Physical AI – Europe / United States | Physical AI / Transformers / Robotics
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Head of Physical AI – Europe / United States | Physical AI / Transformers / Robotics
We are currently partnered with a fast-growing technology company building the data, evaluation, and deployment layer for Physical AI. The company works across multimodal robot and human data, annotation and assurance, model evaluation, and the systems required to turn physical-world experience into useful robot behaviour. Its approach is hardware- and model-agnostic, with a strong focus on measurable improvements in real-world performance rather than benchmark.
Key responsibilities
- Set the overall Physical AI research and engineering strategy, defining a focused roadmap aligned with company objectives
- Identify and prioritise high-value research bets with clear hypotheses, baselines, milestones, success measures, and stop criteria
- Decide which capabilities should be built internally, adapted from existing models, licensed, or accessed through strategic partners
- Own the technical direction across multimodal and embodied data, Transformer-based models, robot policies, evaluation, and physical deployment
- Design, adapt, train, and evaluate Transformer-based systems for embodied tasks
- Make architecture decisions across tokenisation, sequence structure, attention, multimodal fusion, temporal context, action representation, and model heads
- Select appropriate vision-language backbones and determine how they should connect with action, state, perception, and world-model components
- Own pretraining, fine-tuning, post-training, and model adaptation strategies where appropriate
- Define training mixtures across robot demonstrations, human demonstrations, video, language, simulation, synthetic data, and other sources
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Key requirements
- Deep, direct experience with Transformer-based machine learning systems
- Proven personal ownership of material architecture or training decisions within substantial Transformer-based systems
- Experience with one or more areas such as Vision Transformers, vision-language models, vision-language-action models, multimodal foundation models, Decision Transformers, Diffusion Transformers, Video Transformers, world models, or Transformer-based perception, planning, or control
- Ability to explain in technical depth how model inputs and outputs were represented, how tokenisation and multimodal fusion worked, why architectural choices were made, how temporal context was handled, and which objectives and losses were used
- Experience constructing training data mixtures and diagnosing training, optimisation, data, and distribution-shift problems
- Demonstrated ability to connect model changes to measurable improvements in real task performance
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Keywords
Head of Physical AI / Physical AI / Robotics / Artificial Intelligence / Machine Learning / Transformers / Vision Transformers / Vision-Language Models / Vision-Language-Action / VLA / Multimodal AI / Robot Foundation Models / Embodied AI / Computer Vision / Deep Learning / PyTorch / Robot Learning / Imitation Learning / Reinforcement Learning / Diffusion Models / Flow Matching / World Models / Robot Policies / Autonomous Systems / Multimodal Data / Model Evaluation / AI Research / AI Engineering / Technical Leadership / Research Leadership / Robotics Engineering / Model Training / Foundation Models / Data Strategy / Sim-to-Real / ROS / ROS 2 / CUDA / TensorRT / ONNX / GPU Optimisation / Edge AI / Real-Time AI / Model Assurance / AI Safety / Physical Systems / Europe / United States / Global
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