Head of Physical AI

Job not on LinkedIn

🔥 29 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

🤖 Artificial Intelligence

👻 Ghost score 21%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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📋 Description

• Establish and lead the AI research and engineering function • Define which Physical AI problems the company pursues and how results are evaluated • Remain directly involved in designing models and experiments, writing or reviewing code, inspecting data, debugging training runs, analyzing failures, and reviewing robot rollouts • Define a focused Physical AI research and engineering roadmap with hypotheses, baselines, milestones, success measures, and stop criteria • Select model families to train, adapt, or evaluate and decide whether to build internally, use open models, license technology, or work through partners • Design, adapt, train, and evaluate Transformer-based systems for embodied tasks • Work across multimodal and embodied data, 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 sensors, modalities, annotations, data mixtures, coverage, and quality controls • 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 operating environment • Design safe, staged physical testing and deployment plans with supervision and rollback mechanisms • Recruit and lead a small team of 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 to customers, partners, and investors • Within 90 days: audit data, evaluation assets, partnerships, and model opportunities; select research bets; establish a baseline and evaluation suite; define physical validation; present a 12-month roadmap • Within six months: train, adapt, or evaluate a Transformer-based model or robot policy; test on a real robot; document failures • Within twelve months: demonstrate measurable improvement; close the model/data/retraining/evaluation loop; deliver a usable model, benchmark, evaluation system, or deployment; build a small capable team

🎯 Requirements

• Deep, hands-on Transformer expertise; personally made material architecture or training decisions in at least one substantial Transformer-based system • Experience with machine learning for a system that perceives or acts in the physical world • At least one substantial project progressed beyond offline datasets or simulation into a real or operational physical system • Simulation experience qualifies only when paired with credible sim-to-real ownership and physical validation • Strong Python engineering skills • Direct experience with PyTorch or an equivalent deep-learning framework • Ability to read and debug unfamiliar model and training code • Experience designing controlled experiments and analyzing rollout failures • Experience working with large multimodal datasets • Sound judgment around compute, memory, training stability, inference latency, and cost • Experience setting direction for a significant research, model-development, robotics, or cross-functional technical program • Experience making architecture and resource decisions, mentoring or hiring technical talent, stopping weak lines of work, and helping take a result into deployment • A management title is not required, but direct technical ownership is • Strongly preferred: deep Transformer expertise combined with computer-vision experience • Valuable experience includes vision-language-action models, robot foundation models, action tokenization, continuous action generation, diffusion or flow-matching policies, imitation learning, reinforcement learning, world models, cross-embodiment training, robot manipulation, distributed training, inference optimization, ROS 2, sim-to-real transfer, safety-critical systems, open-source work or research publications, early-stage company experience, and work with technical customers or research partners • PhD is valuable but not required; equivalent original model work, technical leadership, and real-system delivery is equally relevant

🏖️ Benefits

• Full-time employment • Remote work in Europe and the United States preferred; exceptional candidates globally considered • Travel as needed

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