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# ML / AI Platform Engineer

- **Company:** Dex
- **Location:** London, UK
- **Salary:** £300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Clusters, Distributed Computing Environment, Parallel Computation, AI Platforms, Low Latency, Machine Learning Operations
- **Published:** August 26, 2026
- **Apply:** https://www.collegerecruiter.com/job/2815182028-ml--ai-platform-engineer

## About the Role

* A strong ML infrastructure or platform engineering background
 * Distributed training, GPU optimisation or large-scale serving experience
 * Comfort going low-level - kernels, profiling, parallelism - when it counts
 * A production mindset: reliability and observability as first-class work

## Description

ML / AI Platform Engineer (Train & Serve at Scale | up to £300k+ TC) · London / Europe

Location: London / Europe

Salary: Total compensation up to £300,000+ plus equity

Platform work gets treated as plumbing at a lot of companies. Not at the ones we work with - frontier labs, fintechs and enterprise-AI teams whose products live or die on training throughput, inference cost and cluster reliability. There, the platform engineer is one of the most consequential hires they make, and the comp reflects it. We know these teams day to day, so we can tell you where the infrastructure genuinely is the product.

The opportunities

Salaries across these roles run up to £300k+, with equity on top. You'd own the systems everything else stands on: the training stack, the GPU clusters, the inference layer, the tooling every ML engineer ships through. Impact is measurable in the units that matter - throughput, latency, reliability - and visible to the whole company when you move them.

You could work on

 * Building and running training and inference infrastructure at scale
 * Owning GPU and cluster performance, throughput and reliability
 * Building the platform and tooling the ML org depends on
 * Taking systems from first build-out to steady production operation

You may have

 * A strong ML infrastructure or platform engineering background
 * Distributed training, GPU optimisation or large-scale serving experience
 * Comfort going low-level - kernels, profiling, parallelism - when it counts
 * A production mindset: reliability and observability as first-class work


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