Lead AI Infrastructure & Distributed Systems Engineer

Building foundation model style AI for physical intelligence and pioneering a system that allows robots to learn complex physical tasks from just a single human demonstration (think "prompting" a robot in the physical world).

Having recently closed a $40M Seed round backed by premier global investors, (marking one of the largest robotics seed investments in European history), they are intentionally keeping their team small, elite, and talent dense. Their primary bottleneck is no longer collecting data, it is the raw speed, scale, and efficiency of training their models.

This business is looking for an elite engineer to "run the show" for their core model training infrastructure. Sitting at the intersection of high performance computing, GPU orchestration, and MLOps, this individual will professionalise the company's infrastructure, eliminate training bottlenecks, and drastically accelerate research cycles.

This is a deeply technical, hands on position. Rather than managing people or executing traditional DevOps/SRE maintenance, the successful candidate will be coding daily, designing scalable distributed systems from scratch, and squeezing maximum throughput out of large scale GPU compute clusters.

The founders are seeking a high agency engineer who thrives in early stage startup environments and prefers broad systems ownership over narrow specialisation.

• Technical Expertise: Strong production background with AWS, Kubernetes, Slurm, PyTorch, and distributed training frameworks. Deep hands-on experience with GPU compute optimisation, cluster scheduling, and high performance networking is essential.
• Relevant Background: Experience in MLOps, AI infrastructure, or HPC. Prior robotics experience is not required, though experience with computer vision pipelines is highly advantageous compared to text-only LLM backgrounds.
• Mindset: A desire to join a founding level team in person in central London, take full ownership of architectural decisions from day one, and capture substantial upside through equity.

Sounds good? Let's have a chat!