Head of Physical AI

Location: Remote — Europe and the United States preferred; exceptional candidates globally will be considered

Employment: Full-time

Reports to: CEO

Role type: Hands-on technical leader and team builder

Travel: As needed

About our Client

Our client builds 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 that turn physical-world experience into useful robot behavior.

Miraxis is hardware- and model-agnostic. What matters is whether a dataset, model, or method produces a measurable improvement on a real task. The company will build focused model and evaluation capabilities where they strengthen its data products, demonstrate the value of its data, or solve a clear customer or partner problem.

The role

Our client is looking for a Head of Physical AI to establish and lead its AI research and engineering function.

You will decide which Physical AI problems the company pursues, define how results are evaluated, and remain directly involved in the most important technical work. You will connect four areas that are often treated separately:

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. During your first year, at least half of your time will be spent on direct technical work: designing models and experiments, writing or reviewing code, inspecting data, debugging training runs, analyzing failures, and reviewing robot rollouts.

You will also build a small, focused team of researchers and engineers as the work requires it.

What you’ll do

Define a focused Physical AI research and engineering roadmap with clear hypotheses, baselines, milestones, success measures, and stop criteria.

Select the model families Miraxis should train, adapt, or evaluate and determine when to build internally, use open models, license technology, or work through partners.

Personally design, adapt, train, and evaluate Transformer-based systems for embodied tasks.

Work across areas such as vision-language-action models, multimodal Transformers, robot foundation models, action representation, imitation learning, reinforcement learning, world models, cross-embodiment transfer, and robot-policy evaluation.

Define the sensors, modalities, annotations, data mixtures, coverage, and quality controls required to train and evaluate selected models.

Measure how data quality, diversity, and composition affect model behavior and real-world task performance.

Establish reproducible offline and real-world evaluation systems, baselines, held-out conditions, and release gates.

Protect evaluations against leakage, overfitting, and weak or misleading success criteria.

Take projects from problem definition through training, hardware integration, and real-world validation.

Analyze failures across data, perception, models, control, hardware, and the operating environment.

Design safe, staged physical testing and deployment plans with clear supervision and rollback mechanisms.

Recruit and lead a small team of complementary researchers and engineers.

Lead architecture, experiment, code, rollout, and failure reviews.

Translate customer and partner needs into testable technical requirements.

Communicate technical strategy, evidence, uncertainty, and limitations clearly to customers, partners, and investors.

What we’re looking for

Deep, hands-on Transformer expertise

This is a hard requirement. You must have personally made material architecture or training decisions in at least one substantial Transformer-based system, such as:

Vision Transformers

Vision-language or vision-language-action models

Multimodal foundation models

Decision Transformers

Diffusion Transformers

Video Transformers

World models

Transformer-based perception, planning, or control systems

You should be able to explain how you represented and tokenized inputs and outputs, fused modalities, structured attention and temporal context, selected losses, built data mixtures, distributed and monitored training, diagnosed failures, and changed the system to improve task performance.