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AI Infrastructure Engineer
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Job Description
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.
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Key Responsibilities
- Coding Daily: The successful candidate will be coding daily.
- Designing Scalable Distributed Systems: Designing scalable distributed systems from scratch.
- Maximising Throughput: Squeezing maximum throughput out of large scale GPU compute clusters.
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.
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Relevant Background
- Experience in MLOps, AI Infrastructure, or HPC.
- Computer Vision Pipelines: Experience with computer vision pipelines is highly advantageous compared to text-only LLM backgrounds.
Mindset
- High Agency Engineer: Thrives in early stage startup environments.
- Broad Systems Ownership: Prefers broad systems ownership over narrow specialisation.
- 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.
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