Head of Physical AI

Location: London is an absolute preference, then Boston or San Diego

Employment: Full-time

Reports to: CEO

Role type: Hands-on technical leader and team builder

Travel: As needed

About Miraxis: Miraxis builds the data, evaluation, and deployment layer for Physical AI. We work across multimodal robot and human data, annotation and assurance, model evaluation, and the systems that turn physical-world experience into useful robot behaviour. We are hardware- and model-agnostic. We care whether a dataset, model, or method produces a measurable improvement on a real task.

The role: The Head of Physical AI establishes and leads Miraxis’s AI research and engineering function. You decide which Physical AI problems we work on, define how we test them, and stay directly involved in the most important technical work. You connect four areas that often sit apart:

• Multimodal and embodied data
• Transformer-based models and robot policies
• Rigorous offline and real-world evaluation
• Deployment on physical systems

This is a player-coach role. In the first year, at least half of your time is direct technical work: designing models and experiments, reviewing or writing code, inspecting data, debugging training, examining failures, and reviewing robot rollouts. You will also build a small research and engineering team. Add people only when the work requires them.

Mandate: Turn Miraxis data and technical access into measurable advances in Physical AI systems. You own the answers, and the evidence, to questions such as:

• Which model families should we train, adapt, or evaluate?
• Which data produces meaningful gains in robot performance?
• What coverage, sensors, annotations, and quality controls do the models require?
• Which offline measurements predict real-world robot performance?
• Where should we adapt existing open models rather than train from scratch?
• What should Miraxis build itself, and what should it reuse or obtain through partners?

Responsibilities:

• Set the technical direction: Define a focused research and engineering roadmap. Select a small number of high-value bets with clear hypotheses, baselines, milestones, and stop criteria. Decide what we build, adapt, license, or access through partners. Drop work that no longer has a strong case.
• Own Transformer research and engineering: Design, adapt, train, and evaluate Transformer-based systems for embodied tasks. Make the architecture and training decisions yourself. Start from strong existing models and baselines. Train from scratch only when evidence supports the cost. Build or review the critical code.
• Connect models to data strategy: Define the data needed to train and evaluate the selected models. Specify sensors, modalities, annotations, mixtures, and quality controls. Measure the effect of data quality, diversity, and composition on model behaviour. Distinguish data volume from data value.
• Own evaluation: Establish offline and real-world evaluation systems, baselines, held-out conditions, and release gates. Guard against leakage, overfitting, and weak success definitions. Compare offline metrics with real-robot outcomes.
• Move work onto physical systems: Take projects from problem definition through training, hardware integration, and real-world testing. Design safe test plans and staged deployment. Analyse failures across data, perception, model, control, hardware, and environment.
• Build a small technical team: Recruit a few complementary researchers and engineers. Lead architecture, experiment, code, and failure reviews. Keep the team focused on validated results, not paper count, benchmark theatre, or headcount.
• Represent the function and own responsible development: Explain the strategy to customers, partners, and investors. Convert customer needs into testable technical requirements. State uncertainty and limits clearly. Define safety constraints, supervision, and rollback paths for physical trials.

What success looks like:

• 90 days: Audit current data, evaluation assets, partnerships, and model opportunities. Define one or two focused research bets with hypotheses, baselines, data, compute, success measures, and stop criteria. Establish a reproducible model baseline. Define an initial evaluation suite with a path to physical validation.
• Six months: Train, adapt, or rigorously evaluate at least one relevant Transformer-based model or robot policy. Confirm or reject at least one important hypothesis. Establish a repeatable data-to-training-to-evaluation workflow.
• Twelve months: Demonstrate a measurable model or evaluation improvement attributable to Miraxis data, methods, or assurance systems. Close the loop between model failures, data decisions, retraining, and re-evaluation. Deliver at least one model, benchmark, evaluation system, or deployment a customer or partner can use.

Required qualifications:

• Deep, direct Transformer expertise: This is a hard requirement. You must have personally made material architecture or training decisions in one or more substantial Transformer-based systems.
• Physical AI or embodied-system experience: You have worked on machine learning for a system that perceives or acts in the physical world.
• Hands‑on technical ability: You write and review high-quality Python. You work directly with PyTorch or an equivalent framework.
• Technical leadership: You have set direction for important technical work.
• Evaluation, communication, and judgment: You design held‑out tests, run controlled comparisons, find leakage and confounds, and report results with appropriate uncertainty.

Strong preference for Computer Vision: We prioritise candidates who combine deep Transformer expertise with deep computer‑vision experience.

Education: A PhD in machine learning, computer vision, robotics, computer science or a related field is valuable but not required. An equivalent record of original model work, technical leadership, and real‑system delivery is sufficient.