Artificial Intelligence Jobs

Wayve · London

Sets technical strategy and roadmap for fault detection and fallback systems in an autonomous driving stack, designing and training detection mechanisms for robust driverless operation. Leads integration into the shared driving stack, aligning ML, inference optimisation and software teams, and mentors through architecture reviews. Works with large-scale training and fleet data in a small, high-impact team.

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

Designs, builds and maintains AWS and on-prem infrastructure used by ML engineers, data scientists and product teams, with a strong focus on Kubernetes, Helm, Terraform/OpenTofu and GitOps delivery. Also handles platform service requests, incident diagnosis and reliability improvements, working across engineering teams in a surgical AI centre of excellence.

PhysicsX · London

Builds and validates high-fidelity multiphase CFD and coupled CFD-DEM models, covering geometry clean-up, meshing, simulation, post-processing and experimental validation for granular and particle-laden systems. Automates parametric CAD and simulation pipelines for design optimisation and DoE, generating datasets for ML surrogate models on cloud and HPC resources, while working on-site with customers and mentoring junior colleagues.

Faculty · London

Builds client-facing products that visualise insights from data scientists, working in cross-functional delivery teams within the National Security and AI Safety unit. Owns frontend standards and reusable component libraries across the company, sets up deployment infrastructure, and mentors other engineers. Requires React, TypeScript, ES6, HTML/CSS3 and data visualisation such as d3, with cloud exposure to AWS, GCP or Azure.

Wayve · London

Builds virtual vehicle models using physics-based and data-driven techniques, validating them against on-road data and converting them into efficient software components. Runs track and partner experiments to improve model accuracy, and integrates models into simulation, hardware-in-the-loop and correlation pipelines across robotics and product teams.

Faculty · London

Build and scale AI-powered systems for public sector clients, owning the full stack from Python/FastAPI backends to TypeScript and React or Vue frontends. Work in cross-functional teams with data scientists and product managers, deploying to AWS, Azure or GCP with Docker and building data pipelines and cloud infrastructure. May require UK Security Clearance and occasional on-site work with government clients.

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

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.

Harnham - Data and Analytics Recruitment · United States · US$200,000 – US$225,000 pa

Build and own core platform capabilities including application infrastructure, APIs and shared services, developing full stack applications in Python, React and TypeScript. Surface machine learning and predictive analytics insights through intuitive interfaces and integrate AI/LLM workflows into production, working in a small innovation team with significant architectural ownership.

Carrington Recruitment Solutions · London · £95,000 – £105,000 pa

Hands-on lead role guiding a small development team across a portfolio of applications in a highly regulated professional services environment. Involves C#, .NET, Python and Azure PaaS work, with a strong AI focus spanning GenAI tools, Co-Pilot Studio and taking experiments through to production-grade solutions, while shaping delivery and engineering standards alongside Product and Architecture.

Profile 29 · Witney · £45,000 – £65,000 pa

Contribute to the design, development, verification and certification of embedded safety-critical systems providing runtime assurance for autonomous platforms. Define verification strategies, build test frameworks and certification evidence aligned with DO-178C, and perform requirements-based testing and traceability. Work in a specialised team on mathematically verifiable software foundations for aerospace and land autonomy.

Profile 29 · Witney · £45,000 – £65,000 pa

Design and develop safety-critical embedded software for autonomous systems, building runtime assurance and fail-safe architectures using SPARK Ada and high-integrity processes. Work spans verification, validation and certification activities for DAL-A/DAL-B software across aerospace and land autonomy platforms, scaling prototype capability to enterprise deployment.

Flarebright · Bicester · £39,000 – £49,000 pa

Contributes across the full development lifecycle, from concept and algorithm design to system integration, testing and troubleshooting of safety-critical navigation and autonomy software in C++. Works with a multidisciplinary team on real-time embedded systems, hardware/software-in-the-loop testing and flight test activities, producing design documentation and test reports.

PhysicsX · New York, United States · US$150,000 – US$250,000 pa

Builds and deploys customer-facing applications on top of physics AI models, working directly with clients, data scientists and ML engineers. Involves full-stack work: FastAPI REST APIs and Python microservices, plus cloud deployment with Helm, Kubernetes, Terraform and AWS/Azure. Expect on-site travel to customer sites for 3-4 weeks per quarter.

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.