Senior ML Platform Engineer - Kubernetes, GPU, Equity

CuspAI; Greater London; On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Equity in CuspAI

28 days holiday

Professional development budget

Diversity & inclusion

Job summary

CuspAI is seeking a Platform Engineer to architect and maintain the ML infrastructure that powers our researchers. You will enable scalable training, evaluation and deployment of models, build robust CI/CD pipelines, registries and experiment tracking within a cloud‑native Kubernetes platform.

You will drive automation, ensure reliability, optimise costs, and collaborate with ML researchers, chemists and software engineers to advance materials science with cutting‑edge AI infrastructure.

Qualifications

• You are a builder of tools and infrastructure who enjoys making life as easy as possible for the teams.
• You stay up to date with the latest relevant technologies, tools, and open source projects.
• You are a seasoned engineer with deep expertise in building and maintaining ML infrastructure, ideally in start‑up environments.
• Experience with ML workloads on Kubernetes and multi‑cloud platforms (AWS, GCP, neoclouds).
• Strong Python and/or Go programming skills for infrastructure automation, not just scripting.
• Expert‑level proficiency in IaC using Terraform, Helm, Kapitan, and GitOps workflows.
• Experience operating distributed ML platforms for JAX and/or PyTorch; GPU infrastructure experience.

Responsibilities

• Build the ML Infrastructure Platform on a cloud‑native stack that enables researchers to train, evaluate, and deploy models at scale.
• Own the MLOps stack: CI/CD pipelines, model registries, experiment tracking, and deployment automation.

Skills

Kubernetes

Python

Go

IaC

MLOps

GitOps

Cloud platforms

Tools

Terraform

Kapitan

Helm

GitOps workflows

Flyte

Job description

CuspAI is seeking a Platform Engineer to architect and maintain the ML infrastructure that powers our researchers. You will enable scalable training, evaluation and deployment of models, build robust CI/CD pipelines, registries and experiment tracking within a cloud‑native Kubernetes platform.

You will drive automation, ensure reliability, optimise costs, and collaborate with ML researchers, chemists and software engineers to advance materials science with cutting‑edge AI infrastructure.