Artificial Intelligence Jobs

Hadean · London

Lead a new AI-powered defence product team, line-managing AI/ML, simulation, infrastructure and web engineers while staying hands-on in TypeScript, React and Node. You own delivery end-to-end, hiring and people development, partnering with Product and technical leads, with hybrid working one day a week in Shoreditch.

Reperio Human Capital · Dublin, Ireland · £388 – £474 pd

Hands-on engineering role owning AI/ML initiatives end-to-end, from solution design and experimentation through deployment, monitoring and optimisation. Involves building production ML and GenAI solutions including RAG architectures, working with Snowflake and cloud data platforms, and developing Python and R software with strong MLOps, CI/CD and DevSecOps practices. Collaborates with data, product and business teams to turn ambiguous problems into scalable, secure solutions.

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.

Wayve · London

Train, debug and improve computer vision and 3D perception models for driver assistance, working across the full ML lifecycle from data and training to evaluation and iteration. The role spans latency-constrained on-car perception and large-scale offline work with lidar, maps and 3D reconstruction to build auto-labelling data flywheels.

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.

Faculty · London

Lead the design, build and deployment of production-grade machine learning systems for clients across pharma, healthcare and other sectors. Work spans architectural decisions, scalable infrastructure, MLOps standards and mentoring junior engineers, with a strong client-facing advisory element. Requires deep Python, TensorFlow or PyTorch, and cloud plus Docker/Kubernetes experience.

Ocado · Kraków, Poland

Senior ML engineer role in a cross-craft data science team building prediction, ranking and personalisation systems for an e-commerce platform. Owns the full ML lifecycle on production systems, from experimentation and training through serving, monitoring and MLOps, while coaching others and shaping reusable platform building blocks.

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.

Harnham - Data and Analytics Recruitment · London · £100,000 – £140,000 pa

Build and lead multimodal AI applications spanning text, audio and video, including AI-generated avatars and automated content summarisation, within a growing AI team in a mature data function. The role involves translating business needs into scalable, production-deployed AI products and collaborating with a multidisciplinary team, with technical leadership duties at Lead level.

eFinancialCareers · London

Design and build AI-powered tools, automations and integrations using Python, PowerShell and APIs, exploring Agentic AI and LLM use cases across internal business functions. Advise on embedding AI into enterprise architecture within a regulated investment management environment, working with engineering, product and business teams. Distinct for its focus on engineering productivity and architecture strategy rather than traditional ML modelling.

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.

WeDoTech · London · £800 – £1,000 pd

Senior hands-on staff engineer driving adoption of Agentic AI and production ML across multiple engineering teams. Defines reusable architectures and patterns, builds production RAG and LangGraph/LangChain systems, and improves AI evaluation, observability, MLOps and model-risk controls in a regulated environment.

MFK Recruitment · Brentford · £70,000 – £95,000 pa

Design and deploy production-grade computer vision systems using deep learning techniques such as CNNs and Vision Transformers, translating research into robust AI solutions for defence and security applications. Work across the full development lifecycle, from model design and training to deployment on cloud and edge platforms. Collaborate with researchers, engineers, and customers in a technically challenging environment focused on real-world autonomy and perception systems.

Hays Technology · London · £100,000 – £110,000 pa

Hands-on role building the backend services, APIs and data pipelines behind a new AI layer for an investment management firm's portfolio platform. Involves LLM applications such as agent workflows, tool use and RAG, plus security controls, automated tests, AI evaluations and monitoring of accuracy, latency and cost in production.

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.

Relation Therapeutics · London

Design and implement generative models that learn intervention effects from single-cell perturbation and multiomic data, focusing on counterfactual prediction, generalisation and experimental design. Work in interdisciplinary teams with experimental biologists to validate computational hypotheses and guide the highest-signal next experiments, using modern ML tooling including agentic workflows.