Artificial Intelligence Jobs

Isomorphic Labs · London

Lead pharmacology and translational biology strategy for large-molecule biologics, designing in vivo efficacy and complex in vitro functional assays and acting as Biology Lead on discovery projects. Coordinate internal workflows and external CROs to drive go/no-go decisions, while feeding pharmacology data back into generative AI design loops with ML and computational biology teams.

Isomorphic Labs · London

Research engineers translate frontier research concepts into practical implementations, building and optimising state-of-the-art AI models such as Transformers, GNNs and diffusion models, plus the codebases, data pipelines and infrastructure for training and evaluation. They design and run experiments, analyse model performance, and advise on bringing models to production, working closely with research scientists in a cross-disciplinary team.

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.

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.

CrowdStrike · Ireland

Security researcher role on CrowdStrike Intelligence's Advanced Research Team, performing vulnerability research and exploit development across a wide range of targets. Day-to-day work includes analysing vendor patches, identifying exploits in large file collections, reverse engineering, and building automated data collection and analysis tooling, alongside publishing research and supporting other intelligence teams.

CrowdStrike · United Kingdom

Join the Advanced Research Team to conduct vulnerability research and exploit development, analysing vendor patches to identify vulnerabilities and build proof-of-concept exploits. The role also involves reverse engineering, investigating 0-day and n-day exploitation, and developing automated tooling to process and analyse data at scale, with results published on the blog or at conferences.

Wayve · London

You'll define and manage the technical architecture for integrating Wayve's AI Driver into OEM and Tier 1 vehicle platforms, covering software–vehicle interfaces, sensors, middleware, ECUs, timing, diagnostics and validation. The role sits between core AI architecture and customer programs, turning ambiguous requirements into interface contracts, ownership boundaries and verifiable specifications.

Medtronic · London · £76,800 – £115,200 pa

Designs, benchmarks and optimises deep learning models for computer vision and surgical video understanding, focusing on low-latency inference on edge and cloud devices. Works with ML, platform and embedded engineers on end-to-end video pipelines, using PyTorch, TensorRT and techniques like pruning and quantization. Distinct for its operating-room, safety-critical medical imaging context.

Helsing · London

Builds computer vision models for object detection, recognition, classification and tracking, extending state-of-the-art architectures and running large-scale multi-GPU training on sensor data. Optimises models for real-time performance on constrained target hardware and collaborates with research, engineering and product teams to shape perception roadmaps.

Relation Therapeutics · London

Builds and scales generative and predictive models of cellular behaviour, implementing and optimising large neural networks and distributed training infrastructure. Acts as a bridge between ML research and production, profiling GPU workloads and optimising inference for use by data scientists and biologists on multi-modal biological data.

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.

Scale AI · United Kingdom · US$300 pa

Research Advisors apply domain expertise in finance, law or medicine to design and evaluate frontier generative AI systems, building benchmarks and computable frameworks for model evaluation. They partner with product, research and GTM teams, support executive client engagements, and co-author technical reports and papers. The role is a flexible part-time contract within an interdisciplinary expert network.

Maxwell Bond · London · £150,000 – £175,000 pa

Hands-on role building LLM agent and retrieval systems on a small UK core team, reporting to the Head of Engineering. Work spans LangGraph/LangChain workflows, RAG pipelines with Qdrant and GraphRAG on Neo4j, model integration across Anthropic, OpenAI and Bedrock, plus evaluation harnesses and observability. Token cost, latency and auditability are treated as engineering constraints throughout.