Lead AI Infrastructure & Distributed Systems Engineer

London Source: WhatJobs

See advert for pay

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