Machine Learning Engineer PyTorch LLM

Machine Learning Engineer (PyTorch LLM) London onsite to £110k
Do you have expertise with Machine Learning in production?
You could be progressing your career at a London based tech start-up with £5 million in recent pre-seed funding, in an impactful role that you'll shape. The product is an AI agentic based platform that writes production grade code.
What's in it for you:
Salary to £110k
Equity / stack options
30 days holiday (+ Bank Holidays)
Daily lunch, monthly breakfasts
Dog friendly office
Pension
Monthly socials
Impactful role that you can shape and influence
Your role:
As a Machine Learning Engineer you'll take open-source LLMs (code and general models) and turn them into high-performance software engineer agents using supervised fine tuning and large scale reinforcement learning. This isn't prompt engineering. You'll design and run serious training experiments across multi-node GPU clusters, build RL loops where models write code and get rewarded (or penalised) by real test outcomes and push long-context and MoE style architectures to their limits.
You'll work hands-on across the full stack: custom PyTorch dataloaders, distributed training (DDP/FSDP), experiment tracking, debugging NCCL issues at 2am, and squeezing performance out of multi-GPU jobs. You'll help design opinionated reward functions that reflect what great engineering actually looks like, not just benchmark scores.
You'll extend benchmark suites, test models on real world repositories, analyse failure modes and feed insights back into data and training strategy. Collaborating with infrastructure, product and research teams you'll contribute to decisions about what to train next and how to measure results.
Location / WFH:
You'll be based in the London, dog friendly office on a fulltime basis, with daily catered lunch, working hours 0900-1700 (with no expectation to do more).
About you:
You have strong experience with training deep learning models in production
You have an indepth knowledge of PyTorch including hands-on experience with torch.distributed (DDP/FSDP-style training, distributed data loading, gradient scaling, etc.)
You have experience of training large sequence models or LLMs at scale
You have a software engineering background with Python, also familiar with TypeScript and / or Golang
You have distributed systems / training ops experience including practical experience running multi-node jobs on GPU clusters (Slurm, Kubernetes, or managed cloud equivalents) and are familiar with GPU performance tuning: memory usage, mixed precision, throughput vs. latency tradeoffs
You have experience within a reinforcement learning environment
You're collaborative with great communication skills
You are degree educated to BSc / MSc in a relevant discipline
Apply now

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