Artificial Intelligence Jobs

Thought Machine · London

Owns and resolves high-priority incidents across hosted SaaS and bank-hosted Vault deployments, acting as a domain expert in Vault Core. Builds tooling, dashboards and automation to improve observability and support delivery, partnering with Engineering and Product on platform stability. Leads escalations end-to-end and mentors across client, product and engineering boundaries.

Thought Machine · London

Guardian of mission-critical production infrastructure powering core banking and payments platforms for major financial institutions. Involves automating fleet management, designing disaster recovery and capacity planning, participating in a global on-call rotation, and mentoring engineers while working with senior stakeholders and SaaS customers.

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.

Faculty · London

Owns and develops product and service design as a core discipline within the Applied AI division, delivering billable client engagements and anchoring project deliverables. Builds executive relationships with client leadership, defines skill and progression frameworks, and partners with business development to shape multidisciplinary teams. Balances strategic delivery with building an entrepreneurial design function.

The University of Manchester · Manchester · £48,337 – £59,390 pa

Develops and applies machine-learning and AI methods to protein engineering and enzyme design, analysing large biological, chemical and experimental datasets and building predictive models and computational tools. Works within an interdisciplinary group spanning the Manchester Institute of Biotechnology and Department of Chemistry, providing ML and data-science expertise, training and support to postdocs and students, and contributing to publications and funding bids.

Capital One · Loughborough

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, manage projects, communicate with stakeholders and mentor junior team members, taking ideas from prototype to production in a hybrid Nottingham team.

Capital One · Lincoln

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 involves setting technical direction, mentoring junior team members, and driving prototypes through to production in collaboration with business stakeholders.

Isomorphic Labs · London

Join the Computational Sciences team to design novel large-molecule therapeutics using a cutting-edge AI drug design engine. You'll develop machine learning methods to predict biologics properties from sequence, benchmark models, and analyse lab data to guide early discovery through candidate selection.

Isomorphic Labs · London

Lead all externalised drug discovery and preclinical lab operations, building and scaling a global CRO infrastructure for an AI-first drug design platform. Oversee experimental throughput, data QC and turnaround times, embed data-generation-by-design standards with Data Engineering, and manage vendor governance, budgets and a team of CRO Scientific Leads, Operations Managers and Compound Managers.

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.

Harnham - Data and Analytics Recruitment · London · £120,000 – £150,000 pa

Lead and build a data science function within a fintech, owning strategy across risk, affordability and lending decisioning. Design, build and deploy machine learning models using credit risk, Open Banking and customer data, working with Product, Engineering and Credit Risk teams. Requires hands-on Python, SQL and MLOps experience plus credit risk modelling in UK consumer lending.

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.

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.

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.