Artificial Intelligence Jobs

NVIDIA · United Kingdom

Develops algorithms and optimizations for NVIDIA's LPX inference and compiler stack, building high-performance runtime and compiler components and mapping large-scale inference workloads onto future hardware. Works at the intersection of compilers, systems and deep learning, using LLVM/MLIR and frameworks like PyTorch and TensorFlow, and collaborates with hardware architects on codesign.

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.

NVIDIA · Bristol

Internship on NVIDIA's embedded computing team in Bristol, working with Tegra SoC, DGX Spark and the Jetson Embedded Platform. The role involves building Linux kernel and user-space software, improving the Jetson Developer Kit experience, and accelerating deep learning and neural network technologies for autonomous vehicles, robotics and analytics alongside hardware and software engineers.

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 Oxford · Oxford · £39,424 – £43,984 pa

Develops 3D reconstruction and multi-view data fusion methods for neonatal cranial ultrasound, validating them on data from the neonatal intensive care unit. Works at the intersection of 3D computer vision, neural rendering and medical image analysis, including physics-informed rendering models for view-dependent acoustic appearance. Based in a multidisciplinary lab alongside deep learning researchers, software engineers and neonatologists.

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.

Helsing · Berlin, Germany

Builds and benchmarks computer vision systems for scene matching, geo-registration, SLAM, visual odometry and 3D reconstruction, adapting state-of-the-art geometric deep learning to real-world conditions and constrained hardware. Works within a scene understanding and localisation team, collaborating across research, engineering and product to integrate outputs into navigation and decision-making pipelines.

Helsing · Barcelona, Spain

PhD research internship tackling open problems in spatial perception and localisation within the Spatial Intelligence Team. You survey and prototype state-of-the-art methods for scene matching, SLAM/VIO and 3D reconstruction, benchmarking ideas against real-world data and compute constraints, and report findings to a Europe-distributed team.

Wayve · London

Post-train, distill and evaluate GAIA-family large video diffusion world models for synthetic driving data, covering embodiment and pose transfer. Own large-scale GPU generation pipelines and land synthetic data into the driving-model training stack, working between ML research and platform engineering.

Latent Labs · London

Architect novel generative models to design functional proteins, working in an interdisciplinary team of ML researchers, protein engineers and biologists. The role spans data curation, evaluation metrics, rapid prototyping, cloud training and inference, and planning wet lab testing campaigns, with about 10% of time for self-development.

Latent Labs · London

Architect novel generative models to design functional proteins, working in an interdisciplinary team of ML researchers, protein engineers and biologists. The role spans data curation, evaluation metrics, rapid prototyping, cloud training/inference optimisation and planning wet lab testing campaigns, with about 10% of time for self-development.

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.

Latent Labs · London

Customer-facing engineering role deploying frontier generative biology models into pharma and biotech environments, owning the full lifecycle from technical scoping to production delivery. Involves building API integrations, data pipelines and model-serving infrastructure on AWS with Docker and Kubernetes, plus on-site work with international partners.