Research ML Engineer — Applied Language Systems

Grace Kim

Contract-to-hire · Mid-level · London

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About the role

The Applied Language Systems Lab is a research group within a UK university\'s Department of Computer Science. Our group has five faculty members, fourteen PhD students, and three postdoctoral researchers. Our work focuses on domain-specific language model adaptation: how pre-trained LLMs can be reliably fine-tuned, evaluated, and deployed in high-stakes domains — currently legal, medical, and regulatory compliance. We have recently established a spin-out company to commercialise the most mature strand of this research, focusing on LLM-based document review and compliance checking for financial services and legal firms. The spin-out has two pilot customers, a pre-seed raise, and the ambition to grow into a serious product company. We are hiring a Research ML Engineer who will sit formally within the lab but whose primary focus is on the engineering work that supports the spin-out. This means building the infrastructure to fine-tune and evaluate models at the scale the product requires, and working closely with our researchers to turn research outputs into something a customer can use. The role starts as a 12-month contract. Subject to the spin-out\'s fundraising trajectory and the individual\'s interest, we expect to offer a permanent position within the spin-out at the end of the initial term.

Responsibilities

Build fine-tuning pipelines for domain-specific LLM adaptation across legal and compliance document types
Design and implement evaluation frameworks that measure model quality in ways that matter to the pilot customers
Develop the RAG infrastructure underpinning the product\'s document review capability
Work with PhD researchers to translate experimental findings into production-worthy implementations
Contribute to technical documentation and reproducibility standards that serve both the research lab and the spin-out

Requirements

3–5 years of ML engineering with a focus on NLP or language model development
Hugging Face — Transformers library, fine-tuning workflows, model evaluation, and the Hub ecosystem — this is your primary working environment
PyTorch for training custom fine-tuning pipelines and understanding what the framework is doing underneath
RAG pipeline design: retrieval, chunking, re-ranking, and evaluation — the spin-out\'s product depends on retrieval quality
LangChain or LlamaIndex for building prototype systems quickly enough to test with pilot customers
Python with the engineering discipline to write research code that can also become product code with reasonable effort
Comfort operating in an academic environment: paper-reading, research discussions, and the ability to translate findings into engineering requirements

Benefits

£68,000 – £84,000 per annum (contract rate) — PAYE or via limited company
12-month initial contract with strong expectation of transition to permanent spin-out role
Remote-first with monthly visits to the university lab for research sessions
Access to university GPU compute and research infrastructure
Opportunity to join a spin-out at a very early stage — equity conversation will happen at transition if the fit is right