Artificial Intelligence Jobs

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.

Faculty · London

Builds and deploys production-grade ML software, tools and infrastructure, creating reusable solutions that accelerate ML delivery and capability testing of frontier AI models. Leads technical scoping and architectural decisions, advises clients and partners, and works with frontier labs on AI safety red teaming and evaluation.

Carrington Recruitment Solutions · London · £95,000 – £105,000 pa

Hands-on lead role guiding a small development team across a portfolio of applications in a highly regulated professional services environment. Involves C#, .NET, Python and Azure PaaS work, with a strong AI focus spanning GenAI tools, Co-Pilot Studio and taking experiments through to production-grade solutions, while shaping delivery and engineering standards alongside Product and Architecture.

Wayve · London

Develops embedded software connecting an AI Driver to vehicle steering, braking and propulsion systems, working hands-on with benches and vehicles. Involves MATLAB/Simulink development, drive-by-wire actuation logic, CAN/CAN-FD diagnostics, OTA updates and ADAS/HMI interfaces, collaborating with OEMs, suppliers and controls engineers.

Wayve · London

You'll design, build and operate shared infrastructure for model access, agent runtimes and observability, taking projects from problem definition through technical design, deployment and ongoing operation. Working with security, IT and engineering teams, you'll create governed access layers, reusable tooling and standards so teams can run AI agents in production without one-off infrastructure.

University of Exeter · Exeter · £21,805 pa

Develop next-generation machine learning models to predict genetic interactions from large-scale perturb-seq data, evaluating graph neural networks and autoencoders for genome-wide scalability. The purely computational project feeds a lab-in-the-loop cycle with pancreatic cancer screens from the AstraZeneca and Cancer Research Horizons Functional Genomics Centre, with results released as open-source software.

University of Hertfordshire · Hatfield · £39,560 – £46,970 pa

Postdoctoral role delivering research on innovative AI/ML algorithms for predictive modelling and optimisation of agricultural applications from heterogeneous data, as part of an Innovate UK-funded collaborative project. Involves developing AI/ML software tools, data management and analysis, supporting the Principal Investigator on project milestones, and contributing to publications and reports.

UCL · London · £46,261 – £48,679 pa

Develop and evaluate novel AI algorithms to quantify brain iron load from standard-format MRI data, with a focus on quantitative susceptibility mapping (QSM). Based in UCL's Hawkes Institute, a multidisciplinary grouping at the intersection of engineering and health, within a top-ranked computer science department.

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.

University of Oxford · Oxford · £39,424 – £47,779 pa

Develops novel statistical and machine learning methods for large-scale genomic, phenotypic and health data, covering areas such as genomic language models, multimodal and federated learning, and multi-ancestry methods. Builds scalable, reproducible research software applied to biobank-scale datasets, evaluates statistical and predictive performance, publishes findings and guides junior researchers in the Palamara Group.

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.

University of Surrey · Guildford · £21,805 pa

Develops next-generation federated machine unlearning algorithms that deliver verifiable, robust erasure without harming model performance or participant privacy. Research spans certified unlearning with formal guarantees, robustness to adversarial relearning, federated-specific benchmarks, and scalable unlearning for foundation models, supervised by Dr Pedro Porto Buarque de Gusmao and Dr Frank Guerin.

University of Exeter · Exeter · £21,805 pa

Develops explainable, trustworthy machine-learning models for early dementia detection, integrating multimodal data such as neuroimaging, cognitive assessments, genetics and biomarkers. Uses latent knowledge-enhanced representations, vision-language agentic frameworks and uncertainty quantification, with clinician evaluation of bias and fairness. Validated through a longitudinal multimodal prediction and explanation system.

Reed · Cambridge

Lead statistical and bioinformatics analysis projects, developing computational algorithms and applying machine learning to functional genomics research. Work with NGS, OMICS and high-throughput datasets alongside laboratory scientists, using Python, R or Julia and libraries such as TensorFlow and PyTorch. The role also involves mentoring and cross-functional collaboration in a multidisciplinary research team.