Founding ML Systems Engineer — Distributed Training

Dex; Greater London; Sur place; GBP 120 000 - 170 000; Plein temps

Résumé du poste

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

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

Qualifications

• 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 on multi-GPU/node clusters.
• Real model-engineering experience in physics/simulation, vision, or LLM systems, with a focus on data-driven system improvement.
• Solid Python backend foundations: API design, workflow orchestration, and practical Docker/Kubernetes/IaC.

Responsabilités

• 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.

Connaissances

Senior hands-on engineer

Distributed training

Python programming

Outils

PyTorch

Docker

Kubernetes

IaC

Description du poste

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

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