Lead AI Infrastructure & Distributed Systems Engineer

Overview

In this early-stage robotics startup, you will own the core model training infrastructure, building scalable systems to train foundation-model style AI for physical tasks. You’ll optimize GPU clusters, orchestration, and data pipelines to remove bottlenecks and accelerate research. This hands-on role sits at the crossroads of HPC, MLOps, and high-performance networking, with full architectural ownership from day one. You’ll work in person in central London as part of a small elite team, with substantial equity upside as the company scales. You drive impact by turning ambitious research into production-ready infrastructure.

Pay / Benefits

• equity upside
• in-person role in central London
• founding-level team
• small elite group
• startup environment

Responsibilities

• Design scalable distributed training infrastructure from scratch
• Eliminate bottlenecks in training pipelines and accelerate research cycles
• Optimize GPU compute, cluster scheduling, and high-performance networking
• Maintain and improve MLOps and AI infrastructure practices
• Work hands-on with AWS, Kubernetes, Slurm, and PyTorch in production environments
• Own architectural decisions and drive system reliability and performance

Key requirements

• 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
• Experience in MLOps, AI infrastructure, or HPC
• Prior robotics experience not required; CV pipelines are advantageous
• In-person, founding-level collaborator in central London
• Equity upside potential

• high agency
• ownership and accountability
• fast-paced startup mindset
• AWS
• Kubernetes
• Slurm

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