Artificial Intelligence Jobs

Faculty · Remote (UK)

Lead the design, build and deployment of production-grade ML systems for defence clients, covering architecture, scalable infrastructure and MLOps standards. Work cross-functionally with engineers, data scientists and commercial teams, acting as a technical advisor and mentoring junior engineers. Requires on-site work with customers 2-4 days per week and eligibility for UK Security Clearance.

Faculty · Remote (UK)

Lead the technical direction and delivery of complex, high-risk AI projects for defence clients, balancing deep ML engineering with team leadership. You'll design scalable ML systems, set architecture, build shared libraries, mentor engineers, and work on-site with customers 2-4 days a week, with the rest remote or from the London office.

Wayve · London

Builds ground-truth datasets and computer vision classifiers that describe complex driving scenes across Wayve's data corpus, drawing on the World Foundation Model to surface rare scenarios and performance gaps. Day to day spans defining annotation tasks and quality criteria, curating datasets, training and evaluating models in PyTorch, and integrating classifiers into enrichment infrastructure for corpus-wide inference.

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.

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.

The Alan Turing Institute · London · £96,000 – £115,000 pa

Provides scientific and strategic leadership for the AI for Physical Systems research area, shaping a portfolio spanning probabilistic modelling, uncertainty quantification, efficient architectures and physics-informed machine learning. Leads a multidisciplinary team of researchers and engineers, taking methods from foundational research through to deployment on real-world resilience challenges, and builds collaborations across academia, government and industry.

Amazon · London

Lead teams of technicians running AWS data centre operations, owning the full infrastructure lifecycle from rack installation and break-fix maintenance to decommissioning. The role spans building new sites, vendor management, health and safety compliance, and adopting high-density GPU, AI accelerator and liquid-cooling technologies, with a focus on automation and operational metrics.

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.

Relation Therapeutics · London

Drive imaging data science for a TechBio company, curating training-ready cell painting and brightfield datasets and designing evaluation frameworks that separate biological signal from technical artefact. Partner with wet lab assay teams from plate design to data delivery, interpret foundation-model behaviour on cellular imaging, and link imaging readouts to single-cell and perturbation data.

OpenAI · London

Conducts research on safety post-training methods such as RLHF, RLAIF, DPO, PPO, reward modeling, adversarial training and synthetic data, then builds evaluations for harms like jailbreaks, hallucination and policy adherence. Works cross-functionally with core training, product, policy, legal and trust and safety teams to ship safety improvements into production models.

Latent Labs · London

Work at the interface between frontier generative models and pharmaceutical/biotech customers, deploying, adapting and fine-tuning models in customer environments. Build production-grade API integrations, model-serving infrastructure and ML data pipelines, while acting as technical point of contact for partners. Combines deep ML research with customer-facing delivery in an interdisciplinary team.

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 Bristol · Bristol · £44,746 – £58,225 pa

The postholder will contribute to world-leading AI research and develop and deliver education programmes, including a new undergraduate degree in Computer Science with AI. The School is particularly interested in candidates with strengths in Multi-Modal AI and Computer Vision, and the role involves publishing in top venues and applying for external funding.

NVIDIA · Zürich, Switzerland

Research and develop techniques to GPU-accelerate deep learning, machine learning and other AI workloads, working with technical experts to analyse and optimise complex AI and HPC algorithms on modern CPU and GPU architectures. The role also involves publishing optimisation techniques via developer blogs or conferences and influencing next-generation hardware and software design.