Artificial Intelligence Jobs

Faculty · London

Builds client-facing products that visualise insights from data scientists, working in cross-functional delivery teams within the National Security and AI Safety unit. Owns frontend standards and reusable component libraries across the company, sets up deployment infrastructure, and mentors other engineers. Requires React, TypeScript, ES6, HTML/CSS3 and data visualisation such as d3, with cloud exposure to AWS, GCP or Azure.

Faculty · London

Build and scale AI-powered systems for public sector clients, owning the full stack from Python/FastAPI backends to TypeScript and React or Vue frontends. Work in cross-functional teams with data scientists and product managers, deploying to AWS, Azure or GCP with Docker and building data pipelines and cloud infrastructure. May require UK Security Clearance and occasional on-site work with government clients.

Wayve · London

Lead a team of ML engineers and applied scientists building synthetic data from generative world models like GAIA-3, turning them into a production engine for training-grade driving experience. Own the loop end to end, from checkpoint and large-scale GPU inference to artefacts landing in driving-model training, while setting technical direction on rig transfer, pose transfer and controllability.

Wayve · London

Lead the Applied Methods Data Science team, partnering with the Autonomy organisation to turn experimental and observational analysis of real and simulated driving into evaluations and insights. Set three-, six- and 12-month roadmaps, review technical proposals for statistical rigour, hire and grow data scientists, and build tooling to scale the team's capability.

Capital One · Derby

Lead the development of proprietary deep learning models to enhance underwriting capabilities, building neural networks such as LSTMs, RNNs and Transformers on structured and unstructured data. The role combines technical direction-setting, stakeholder collaboration and mentoring, taking ideas from prototype through to production in a hybrid Nottingham-based team.

Capital One · Leicester

Lead the development of proprietary deep learning models to enhance underwriting capabilities, building neural networks such as LSTMs, RNNs and Transformers on structured and unstructured data. Set technical direction, mentor junior team members and drive prototypes through to production in collaboration with business stakeholders.

DCV Technologies · Manchester · £550 – £600 pd

Contract data science role focused on building and evaluating AI/ML solutions on Google Cloud Platform, including classical machine learning models and LLM-based applications. Day-to-day work spans prompt engineering, RAG concepts, experiment design, model evaluation and documentation, alongside requirements gathering, code review and troubleshooting with project managers and developers.

Amazon · London

Lead end-to-end delivery of complex AI/ML engagements for enterprise customers, from strategic planning through pre-production deployment and optimisation. Architect solutions on AWS services with a focus on Generative AI using Amazon Bedrock and SageMaker, while mentoring junior consultants and supporting pre-sales scoping. Based in AWS UK offices when not at customer sites.

Vallum Associates · London · £475 – £500 pd

Data scientist role focused on collecting and analysing large datasets, building experimental models and machine learning applications in Python, and performing complex statistical analysis. Involves leading small teams on data science products, developing classical ML and LLM solutions with prompt and RAG techniques, and mentoring junior data scientists. Hybrid working with two days per week in the London office.

Amazon · London

Manage a London-based team of associates producing labelled speech and language data for Alexa and Amazon Echo ML systems. Day to day you assign work, monitor queue adherence, volume and quality, hire and train associates, build weekly schedules across competing workflows, and track quality and utilisation metrics. You liaise with program management and global operations leads on risk mitigation and drive process improvement using Kaizen, Six Sigma or lean methods.

University of Cambridge · Cambridge · £33,662 – £46,970 pa

Lead a workstream within the IMPACT-OC European consortium, building a multi-agent AI system for ovarian cancer that spans imaging, genomics, histopathology and clinical records with privacy-preserving federated learning. The role is jointly based in the Department of Radiology and the Early Cancer Institute, combining multimodal AI pipelines, digital twin technology and clinician-facing deployment across eight partner institutions.

ECM Selection · London · £46,000 – £52,000 pa

Join a national security team building robust AI agents and realistic training environments to defend critical infrastructure from cyber threats. Day-to-day work spans realistic network simulation environments, novel agent architectures, and evaluation tasks, requiring strong Python, Linux and git skills plus a research mindset. Hybrid role with just one day per week in the central London office.

The Francis Crick Institute · London · £47,500 pa

Lead a computational project modelling how driver mutation clones interact with their microenvironment in chronic liver disease and liver cancer. Develop graph neural networks, transformers and generative AI on large-scale spatial transcriptomics, single-cell and imaging data, in a cross-institutional collaboration with the Wellcome Sanger Institute's Lotfollahi Lab.

Bowers Partnership · London · £825 pd

Hands-on VP-level data scientist building generative AI, LLM/NLP and statistical modelling products end to end, from prototype to production, for a boutique investment bank's M&A and advisory bankers. Works in a small team, writing production-quality Python and explaining models and assumptions directly to senior non-technical stakeholders. M&A experience is essential; hybrid with at least three days a week in the London W1 office.

Relation Therapeutics · London

Develops and benchmarks machine learning models for DNA sequence, regulatory elements and genetic variation, including variant effect prediction and representation learning. Integrates sequence-derived representations with transcriptomic, epigenomic and perturbational data to uncover disease mechanisms and support target prioritisation, working within the interdisciplinary Rosalind team.