Artificial Intelligence Jobs

Faculty · London

Lead the design, build and deployment of production-grade ML and LLM systems for national security and AI safety clients, including capability testing of frontier models. Act as a trusted technical advisor, define engineering standards for ML at scale, and mentor junior engineers while working with frontier labs on red teaming and evaluation.

Faculty · London

Lead the design, build and deployment of production-grade ML systems for national security and AI safety clients, including capability testing of frontier models. Act as a technical advisor to customers, define best practices for deploying ML at scale, and mentor junior engineers. Distinct for its focus on red teaming, evaluation and high-stakes government and frontier lab work.

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.

NVIDIA · Switzerland

Develops performance optimizations in deep learning frameworks using JAX, contributing to JAX core components and driving peak performance on NVIDIA hardware. Works with AI researchers to build future-proof models and tooling that bridges numerical computing, simulation and deep learning research. Requires C/C++ and Python skills plus deep knowledge of ML framework internals and CPU/GPU architectures.

NVIDIA · United Kingdom

Develops performance optimizations in deep learning frameworks using JAX, contributing to JAX core components and driving peak performance on NVIDIA hardware. Works with AI researchers to build future-proof models and tooling that bridges numerical computing, simulation and deep learning research. Requires C/C++ and Python, ML framework internals, and CPU/GPU architecture expertise.

Parkside · London · £100,000 – £150,000 pa

Lead the development of AI features in a legal tech SaaS platform, building ML models for document parsing, search, classification and summarisation. Integrate LLMs with guardrails, evaluation and monitoring, and optimise model serving on AWS. Work hands-on in Scrum squads alongside engineering, product and QA.

Entrust · London

Builds and operates production ML systems for document classification and extraction, using LLMs/VLMs and traditional ML to process government identity documents at scale. Owns features from RFC through deployment, optimising GPU inference and training/evaluation pipelines, and mentors engineers within a squad-based team.

Entrust · Lisbon, Portugal

Builds and operates production ML systems for document classification and extraction, using VLM/LLMs and traditional ML to power identity verification for millions of users. Leads technical design from RFC to deployment, optimises GPU inference, and designs training, evaluation and labelling pipelines. Works in a squad-based team alongside Applied Science, Product and Data Science, mentoring engineers and driving reliability and scalability.

Isomorphic Labs · Lausanne, Switzerland

Architect and secure an AI-first drug discovery platform, reporting to the CISO. You will build AI-specific risk frameworks, protect model artifacts, deploy LLM and agentic guardrails, and lead AI security incident response across distributed training and inference infrastructure.

Isomorphic Labs · London

Lead and contribute to core machine learning research applying large language models to drug discovery, developing and refining LLM-driven approaches across biology and chemistry problems. Work spans post-training, reasoning, test-time scaling, tool-use, alignment, agents, reinforcement learning and fine-tuning, with model training and inference engineering at scale. Interdisciplinary collaboration with research scientists and engineers, plus technical mentorship and project leadership depending on experience.