Artificial Intelligence Jobs

Wayve · London

Develops and maintains Wayve's Core Simulation system in modern C++ and Python, integrating driving-stack components, simulated robot systems and vehicle-platform capabilities. Initially focused on high-scale open-loop evaluation, the role involves debugging on-road versus simulated behaviour, improving reproducibility, observability and automated testing, and profiling simulator performance across a shared codebase.

Wayve · London

Lead a London-based team building the on-vehicle experiment platform that lets researchers deploy driving models to vehicles, run experiments and collect training data. You own people, roadmap and delivery, plus partner and OEM relationships, while staying close to embedded Linux systems code in C++ or Python.

Capital One · Mansfield

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.

Capital One · Nottingham

Leads development of proprietary deep learning models to enhance underwriting capabilities, building neural networks such as LSTMs, RNNs and Transformers on structured and unstructured data. Bridges R&D and production, prioritising initiatives with stakeholders and mentoring junior team members.

Isomorphic Labs · Cambridge, United States

Lead DMPK strategy across drug discovery projects, designing screening cascades and mechanistic experiments while integrating PK, PD and toxicology data for dose and safety predictions. Work within an AI-first preclinical team, shaping ADME predictive models and authoring regulatory documents to advance candidates toward first-in-human trials.

Isomorphic Labs · Cambridge, United States

Lead the safety assessment strategy for drug discovery projects from inception to regulatory submission, designing screening cascades and mechanistic experiments for small molecules and biologics. Partner with machine learning researchers to build predictive toxicology models and manage CRO relationships for outsourced wet-lab data generation, while mentoring junior scientists.

University of Surrey · Guildford · £21,805 pa

Develops a computational framework combining computer vision, graphics and machine learning to speed up and improve cinematic camera lens design and simulation. The work covers forward simulation of candidate lens configurations and inverse design of lens architectures against imaging or perceptual objectives, using differentiable rendering and data-driven models. Based at the Vision, Speech and Signal Processing group under Dr Marco Volino and Dr Jean-Yves Guillemaut, with close industry collaboration.

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.

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.

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.

University of Exeter · Penryn · £21,805 pa

Develops physics-aware spatio-temporal graph neural networks for 0–6-hour visibility forecasting, focusing on low-visibility events in southeast England. The work spans six packages, from benchmarking NWP and ML baselines to linking learned representations with atmospheric physics, testing robustness to degraded observations, and operational evaluation with Met Office scientists.

NVIDIA · Bristol

Research and develop techniques to GPU-accelerate deep learning, machine learning and other AI workloads, working with technical experts from industry and academia to analyse and optimise complex AI and HPC algorithms on modern CPU and GPU architectures. The intern also publishes optimisation techniques via developer blogs or conferences and helps influence next-generation hardware, software and programming models.

University of Birmingham · Birmingham · £21,805 pa

Develops a physics-informed AI framework to design and optimise alternative cooling mechanisms and refrigeration cycles, integrating thermodynamic modelling, machine learning and multi-objective optimisation. Experimental work validates selected combinations, with scope to balance computational and lab effort. Based in the School of Chemical Engineering alongside sustainable cooling and energy storage research centres.

University of Exeter · Exeter · £21,805 pa

Develops and evaluates AI, machine learning and deep learning methods for grading retinal fundus images to detect diabetic retinopathy, glaucoma, cataract and AMD. Works with large datasets including EyePACS, UK Biobank and NHS sources, comparing algorithm accuracy against NHS clinicians and optimising explainable models for clinical trust.