Robotics Applied Research Lead (Physical AI)

Location: UK, London; on-site

Company

hirify.global develops commercially scalable and safe hirify.global robots, including the HMND-01 platform for real industrial environments.

What you will do

• Establish and lead the AI research and engineering function for Physical AI, setting a focused roadmap with hypotheses, baselines, milestones, and stop criteria.
• Design, adapt, train, and evaluate Transformer-based systems including vision-language-action models, multimodal Transformers, robot foundation models, world models, and policy models.
• Define data requirements across sensors, modalities, annotations, mixtures, and quality controls, and measure how data quality and composition affect robot behaviour.
• Build offline and real-world evaluation systems, held-out tests, release gates, and comparisons between offline metrics and real-robot outcomes.
• Move projects from problem definition through training, hardware integration, safe staged deployment, and physical testing; analyse failures across data, perception, models, control, hardware, and environments.
• Build and lead a small research and engineering team, represent the function to customers, partners, and investors, and define safety constraints, supervision, and rollback paths.

Requirements

• Deep, direct expertise in Transformer architectures and training, including personally making material architecture or training decisions in a substantial Transformer-based system and explaining representations, tokenisation, modality fusion, attention, temporal context, losses, data mixtures, distributed training, monitoring, and failure diagnosis. Hosted APIs, prompting, or unchanged public recipes do not meet this requirement.
• Experience with Physical AI or an embodied system such as robotics, autonomous vehicles, drones, industrial automation, manipulation, mobile robots, hirify.globals, or wearable systems; at least one substantial project must have progressed beyond an offline dataset into a real or operational physical system. Simulation-only experience is insufficient without credible sim-to-real transfer and physical validation.
• Hands-on ability with high-quality Python, PyTorch or an equivalent framework, unfamiliar model and training code, large multimodal datasets, experiment debugging, rollout analysis, and compute, memory, latency, and cost decisions.
• Technical leadership of important research or engineering work, including architecture and resource decisions, hiring or mentoring, stopping weak work, and taking results into deployment; a management title is not required.
• Ability to design held-out tests, controlled comparisons, and leakage and confound checks, and to communicate uncertainty while separating evidence from belief.
• Strong preference for computer vision expertise, including visual representation learning, VLMs, video and temporal modelling, detection, segmentation, tracking, multi-view or egocentric vision, 3D and spatial reasoning, pose estimation, calibration, localisation, sensor fusion, or real-time vision systems.
• 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.

Nice to have

• Experience with VLA or robotic foundation models, action generation, diffusion or flow-matching policies, imitation learning, reinforcement learning, world models, robotic manipulation, cross-embodiment training, sim-to-real transfer, distributed training, ROS 2, or safety-critical systems.
• Research publications, open-source contributions, early-stage company experience, or direct collaboration with technical customers and research partners.

Culture & Benefits

• Player-coach role with at least half of the first year focused on direct technical work.
• 30+ paid days off, including 23 days of annual leave, UK bank holidays, and additional company closure days.
• Private healthcare with virtual and in-person care.
• Pension scheme with an 8% total contribution: 5% employee and 3% employer on full earnings.
• Free daily breakfast, catered lunch, and snacks in the office.
• Competitive equity, daily collaboration with engineers and researchers, and direct access to founding leadership.