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# Machine Learning Engineer

- **Company:** Cosine
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, Nvidia CUDA, Programming Tools, Distributed Computing Environment, Machine Learning, Open Source Technology, Software Systems, Pytorch, DeepSpeed, Large Language Models, Deep Learning, Kubernetes, SGLang, Slurm
- **Published:** October 16, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-lumen-enterprise-models-swe-focused-llms-cosine-10476890

## About the Role

You may come from machine learning research, applied ML, model training or a closely related engineering background.

You should have:

 * Strong experience training deep-learning models in a production or research environment.
 * Deep practical experience with PyTorch or a comparable framework.
 * Experience with distributed training and multi-GPU systems (FSDP, Deepspeed, …)
 * Strong software-engineering fundamentals and a high bar for correctness and maintainability.
 * Clear communication and the ability to explain technical trade-offs.
 * Experience with large language models is important. You should have worked meaningfully on at least one of, pre-training, SFT, preference optimisation, RLHF or RLVR.

We care more about depth of understanding and evidence of strong technical judgement than about having used every tool listed. Even if you don't tick every box, please consider applying!

A bonus if you have

 * Experience with mixture-of-experts (MoE) architectures.
 * Experience with CUDA.
 * Familiarity with vLLM, SGLang or other high-performance inference systems.
 * Experience running jobs on SLURM, Kubernetes or managed GPU platforms.
 * Experience with code-model benchmarks such as SWE-bench, HumanEval or internal repository-level evaluations.
 * Contributions to open-source model-training, RL or evaluation tools.

## Description

Cosine is building autonomous software-engineering systems for organisations where reliability, security and control matter most.

Our Lumen Enterprise models are designed to work across real codebases. They need to understand unfamiliar architectures, make changes across multiple files, use tools, run tests and produce software that engineers can maintain and trust., You'll work closely with research, infrastructure and product to answer questions such as:

 * What should Lumen learn next?
 * Which data and environments will teach it most effectively?
 * How should we reward correct, maintainable and useful software?
 * How do we train models to work across large repositories and long contexts?
 * How do we know when a model is genuinely better for engineers?

The models you train will write code, use development tools, run tests and receive feedback from real software-engineering environments. Your work will directly influence how Lumen reasons about code, navigates unfamiliar systems and improves through experience., They need to understand context, respect existing architecture, use tools effectively, test their work and recover when their first attempt fails. They need to produce changes that work in the real world, not just on a benchmark.

That behaviour has to be trained.

You'll work directly on the data, environments, rewards and evaluations that shape Lumen's capabilities. Your work will influence the models used by engineers and enterprise teams working on complex and security-sensitive software systems.