Founding Engineer - ML Systems (up to £180k)

Dex; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Dex is partnering with a company that builds foundation models for physics, semiconductors, aerospace, defense, and fusion energy. You’ll be the first dedicated engineering owner for the ML stack, turning prototypes into robust code and building in-house tooling for experiment tracking and hyperparameter optimisation.

You’ll also add distributed training capabilities, own the backend platform delivering these models to customers, and define the engineering culture from day one.

Qualifications

• Senior, hands-on engineer who still ships critical code.
• Proven PyTorch experience across model code, data pipelines, and training loops.
• Experience with distributed training and multi-GPU/cluster setups.

Responsibilities

• Own the entire ML stack: model code, training workflows, and experiment infrastructure.
• Build distributed training capabilities for large-scale model development.
• Integrate independently developed research branches into a production-ready codebase.
• Design and build the backend platform that delivers these foundation models to customers.

Skills

PyTorch

Distributed training

Python backend

Model engineering

Docker/Kubernetes/IaC

Tools

Docker

Kubernetes

IaC

Job description

This role is with one of Dex's trusted partner companies. We work closely with their teams to truly understand their culture, goals, and what they're looking for, so we can match you with the right opportunity and give you context about the role before you commit to a process.

Dex is an AI recruiter agent that helps you run your job search. Tell Dex your stack, seniority, and what you want to build. We will manage your applications and surface other opportunities that are a fit.

The role

This company builds foundation models for extreme physics: the regimes behind semiconductors, aerospace, defence, and fusion energy. Their work accelerates progress in fields where existing simulation tools are too slow or brittle, enabling designs traditional workflows can’t reach. They’re backed by leading investors and tackling problems with global impact.

You’ll be the first dedicated engineering owner for the ML stack, joining a small, highly technical team of researchers. This isn’t a narrow systems role, nor is it pure research; you’ll turn prototypes into robust code, integrate research branches, and build the in‑house tooling for experiment tracking and hyperparameter optimisation. You’ll also add distributed training capabilities and own the backend platform delivering these models to customers, setting the engineering culture from day one.

The work

• Own the entire ML stack: model code, training and evaluation workflows, and experiment infrastructure.
• Build and implement distributed training capabilities for large‑scale model development.
• Integrate independently developed research branches into a coherent, production‑ready codebase.
• Profile and resolve real bottlenecks in training stability and performance, improving system efficiency.
• Design and build the backend platform that delivers these foundation models to customers.

What You Bring

• You are a senior, hands‑on engineer who still writes and ships critical code.
• Strong practical experience with PyTorch across model code, data pipelines, and training loops.
• Proven track record with distributed training (multi‑GPU/node, GPU clusters), understanding associated memory and communication challenges.
• Real model‑engineering experience in physics/simulation, vision, or LLM systems, with a focus on data‑driven system improvement.
• Solid Python platform and backend foundations: API design, workflow orchestration, and practical Docker/Kubernetes/IaC.

Why apply through Dex

This is a rare, high‑impact founding role at an early‑stage company, often hard to find or apply to directly. Dex helps you cut through the noise: sign up once, get matched to this role and others like it, and skip the cold application process. We'll brief you properly on the company and team before you even interview, ensuring a strong fit from the start.

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