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Founding Machine Learning Engineer / YC Start-up / £140,000 - £160,000 - Opus Recruitment Solutions

Founding Machine Learning Engineer / YC Start-up / £140,000 - £160,000 - Opus Recruitment Solutions

On site

London, United kingdom

Mid level

Full Time

26-01-2026

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Skills

Python Data Engineering CI/CD Docker Kubernetes Monitoring Agile methodologies Research Training Machine Learning PyTorch TensorFlow Programming Agile Team Development python programming Large Language Models Natural Language Processing CI/CD Pipelines NLP

Job Specifications

Founding Machine Learning Engineer

£140,000 - £160,000 + Equity

3 days minimum in Central London

Opus are hiring on behalf of a fast-growing, Y Combinator-backed start-up that’s redefining how financial data is processed and understood. Operating at the intersection of AI and enterprise infrastructure, this company is building intelligent systems that extract meaning from complex, unstructured documents at scale. Their platform is already trusted by leading firms in the alternative investment space, and they’re now expanding their machine learning team to accelerate innovation.

This is not a research role. It’s a high-impact product engineering role in forward-deployed style where your work ships into production and is used by customers daily.

Key Requirements

Candidates should bring a minimum of five years’ experience in machine learning engineering, with demonstrable expertise in:

Natural Language Processing (NLP), information extraction, and working with large language models (LLMs)
Python programming and major ML frameworks such as PyTorch or TensorFlow
MLOps practices including containerisation (Docker), orchestration (Kubernetes), and CI/CD pipelines tailored for ML workflows
Utilising AI-enhanced development environments and tools to streamline experimentation and deployment
Cross-functional collaboration with engineering, product, and business stakeholders
Agile methodologies and fast-paced product development environments

Preferred Qualifications

The following will be considered advantageous:

Advanced academic credentials (Master’s or PhD) in computer science or a related field
Experience in training and deploying LLMs at scale
Familiarity with cloud infrastructure and distributed computing environments
Exposure to modern ML tooling such as Modal, Weights & Biases, or Amazon SageMaker
Knowledge of fine-tuning techniques including LoRA, QLoRA, or other parameter-efficient frameworks

Role Overview

The successful candidate will be responsible for designing and implementing machine learning solutions that interpret and structure unorganised financial data. This includes:

Developing models for classification, entity recognition, summarisation, and retrieval
Customising and refining LLMs for specific business applications, ensuring optimal performance and scalability
Collaborating with data engineering teams to prepare and transform large datasets for model training
Building robust ML services with monitoring, retraining, and performance tracking capabilities
Enhancing the organisation’s MLOps infrastructure, including model lifecycle management and evaluation systems
Partnering with product and engineering teams to embed ML capabilities into core platforms
Staying abreast of emerging research in LLMs and agentic AI, and applying relevant innovations to production systems
Supporting team development through code reviews and mentoring junior engineers

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About the Company

Employers, hiring managers, recruiters. Everyone has one thing on their mind. Finding talented people. But… everyone's looking in the same places. At the same time. In the same tiring way. Traditional job advertising always targets the same people. The ones that are actively looking for a new job. The only thing is; the best talent is often taken. But just because they're not searching for a new job, doesn't mean they're not looking for one. What if you knew who's eager to switch jobs before they start looking? Welcome to Jo... Know more