Machine Learning Engineer

I’m working with an exciting, early stage FinTech that is building AI systems for complex, data-intensive industries like commodity trading.They’re looking for an Applied AI/ML Engineer who wants to work close to the founders and engineering team on a small team with significant ownership over how problems are solved.This is not an AI wrapper or prompt engineering role. They’re looking for someone who has genuinely worked with ML models which includes training, fine-tuning, evaluating and improving them and enjoys figuring out difficult problems in new domains.What you’ll be working onDeveloping and deploying LLM-powered systems using PyTorchFine-tuning open-source models using LoRA / QLoRA / PEFTBuilding and improving embedding and reranking modelsDesigning datasets and generating synthetic training dataWorking on semantic search and document intelligence systemsRunning experiments and building robust model evaluation frameworksOptimising models for real-world production use casesWorking closely with domain experts to turn complex workflows into ML solutionsWhat we're looking forYou’ll ideally have hands on experience with several of the following:PyTorchTransformer-based models such as BERT, RoBERTa, Llama, etc.LLM fine-tuningLoRA / QLoRA / PEFTEmbedding modelsReranking / cross-encoder modelsSemantic search / information retrievalDataset construction, cleaning or synthetic data generationML experimentation and evaluationHugging Face / Transformers or similar toolingProduction ML, containers and cloud infrastructureYou don't need to be a researcher who can train a transformer from scratch. They're much more interested in someone who can take existing models, understand how they work, adapt them to a new problem and get them into production. Please note that academic, research or R&D experience is absolutely relevant.Experience with RAG is useful, but this isn't a role for someone whose experience is primarily connecting APIs together with LangChain or similar frameworks.I am particularly interested in people who understand what is happening underneath the RAG stack, how embeddings are trained and evaluated, how retrieval works, how rerankers work, how training data is created and how models can be improved through experimentation.If you've actually trained or fine-tuned models, rather than simply used them, I'd be very interested in speaking with you.The role is 4 days a week hybrid in London and is paying up to the £100k + bonus mark on the top of the band.If interested, please apply or email me directly at tomasz@virtuetech.io