Founding Machine Learning Engineer£140,000 - £160,000 + Equity 3 days minimum in Central LondonOpus 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 RequirementsCandidates 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 TensorFlowMLOps practices including containerisation (Docker), orchestration (Kubernetes), and CI/CD pipelines tailored for ML workflowsUtilising AI-enhanced development environments and tools to streamline experimentation and deploymentCross-functional collaboration with engineering, product, and business stakeholdersAgile methodologies and fast-paced product development environmentsPreferred QualificationsThe following will be considered advantageous:Advanced academic credentials (Master’s or PhD) in computer science or a related fieldExperience in training and deploying LLMs at scaleFamiliarity with cloud infrastructure and distributed computing environmentsExposure to modern ML tooling such as Modal, Weights & Biases, or Amazon SageMakerKnowledge of fine-tuning techniques including LoRA, QLoRA, or other parameter-efficient frameworksRole OverviewThe 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 retrievalCustomising and refining LLMs for specific business applications, ensuring optimal performance and scalabilityCollaborating with data engineering teams to prepare and transform large datasets for model trainingBuilding robust ML services with monitoring, retraining, and performance tracking capabilitiesEnhancing the organisation’s MLOps infrastructure, including model lifecycle management and evaluation systemsPartnering with product and engineering teams to embed ML capabilities into core platformsStaying abreast of emerging research in LLMs and agentic AI, and applying relevant innovations to production systemsSupporting team development through code reviews and mentoring junior engineers