Machine Learning Engineer

VirtueTech Recruitment Group; Greater London; Hybrid; GBP 90,000 - 110,000; Full time

Benefits offered by this job

Hybrid work model

London office access

Job summary

VirtueTech Recruitment Group is seeking a Machine Learning Engineer for a FinTech start-up in London. You will build, retrain and re-rank LLM models, working closely with founders and the core engineering team to own complex problems from ideation to production.

The role emphasizes hands-on model training, fine-tuning and evaluation across semantic search and document intelligence tasks, with a strong focus on making models robust and production-ready.

Qualifications

• Hands-on experience with ML models including training, fine-tuning and evaluation.
• Ability to design data pipelines and synthetic data for model training.
• Experience deploying ML systems in production environments.

Responsibilities

• Developing and deploying LLM-powered systems using PyTorch.
• Building and improving embedding and reranking models.
• Designing datasets and generating synthetic training data.
• Working on semantic search and document intelligence systems.
• Running experiments and building robust model evaluation frameworks.
• Optimising models for real-world production use cases.
• Collaborating with domain experts to translate workflows into ML solutions.

Skills

PyTorch

Transformer-based models

LoRA/QLoRA/PEFT

Semantic search

Dataset construction

ML experimentation

Hugging Face/Transformers

Production ML

Job description

A fascinating FinTech start-up is looking for a Machine Learning Engineer to build, retrain and re-rank their LLM models whilst transforming some complex financial markets.

You get to work up close with founders and the founding engineering 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 on

• Developing and deploying LLM-powered systems using PyTorch
• Building and improvingembedding and reranking models
• Designing datasets and generating synthetic training data
• Working on semantic search and document intelligence systems
• Running experiments and building robust model evaluation frameworks
• Optimising models for real-world production use cases
• Working closely with domain experts to turn complex workflows into ML solutions

What we're looking for

You’ll ideally have hands on experience with several of the following:

• PyTorch
• Transformer-based models such as BERT, RoBERTa, Llama, etc.
• LoRA / QLoRA / PEFT
• Semantic search / information retrieval
• Dataset construction, cleaning or synthetic data generation
• ML experimentation and evaluation
• Hugging Face / Transformers or similar tooling
• Production ML, containers and cloud infrastructure

You 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.