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

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