Artificial Intelligence Jobs

Faculty · London

You will own and evolve the internal delivery platform that supports over 100 data scientists and ML engineers, building MLOps, data science and deployment tooling. Day to day involves infrastructure-as-code, DevSecOps, containerised microservices and integrating core services across AWS, Azure and GCP. The role sits in a small, fast-moving team focused on removing technical friction for customer-facing delivery teams.

PhysicsX · New York, United States · US$120,000 – US$240,000 pa

Builds and deploys deep learning and probabilistic models to predict and control physical systems, working alongside simulation and ML engineers. Owns data pipelines and production deployment, and collaborates directly with customers on-site across multiple regions. Based in Manhattan with 2-3 days per week in office and frequent travel.

Wayve · London

Builds ground-truth datasets and computer vision classifiers that describe complex driving scenes across Wayve's data corpus, drawing on the World Foundation Model to surface rare scenarios and performance gaps. Day to day spans defining annotation tasks and quality criteria, curating datasets, training and evaluating models in PyTorch, and integrating classifiers into enrichment infrastructure for corpus-wide inference.

Wayve · London

Lead a team of ML engineers and applied scientists building synthetic data from generative world models like GAIA-3, turning them into a production engine for training-grade driving experience. Own the loop end to end, from checkpoint and large-scale GPU inference to artefacts landing in driving-model training, while setting technical direction on rig transfer, pose transfer and controllability.

Faculty · London

Scoping, building and delivering bespoke AI solutions for pharmaceutical and life sciences clients, managing individual work streams and translating operational requirements into user stories for engineering teams. Involves open-source analysis, workflow design and close collaboration with data science and ML experts, with a focus on high-stakes, user-centric decision-making.

Darktrace · Cambridge

Conducts independent and collaborative research across large language models, statistical methods and classical machine learning, prototyping ideas and implementing models into the wider software stack. Produces optimised solutions for latency and memory, including deployment on edge devices, working closely with software engineers and security analysts.

Isomorphic Labs · London

Builds greenfield machine learning models and algorithms for drug discovery, designing new architectures, running experiments, and scaling training and inference frameworks. Works in an interdisciplinary team of ML scientists and domain experts, with opportunities to lead projects and mentor others depending on experience.

University of Sheffield · Sheffield · £39,560 – £43,099 pa

Postdoctoral role applying explainable NLP and machine learning to analyse online disinformation narratives, integrate findings into a user-facing verification toolkit, and evaluate commercial chatbots and AI-generated search overviews. The researcher joins the European-funded ANTIGONE project, collaborating with multidisciplinary partners and leading publication of results in high-impact journals and conferences.

The University of Manchester · Manchester · £48,337 – £59,390 pa

Develops and applies machine-learning and AI methods to protein engineering and enzyme design, analysing large biological, chemical and experimental datasets and building predictive models and computational tools. Works within an interdisciplinary group spanning the Manchester Institute of Biotechnology and Department of Chemistry, providing ML and data-science expertise, training and support to postdocs and students, and contributing to publications and funding bids.

University of Hertfordshire · Hatfield · £39,560 – £46,970 pa

Postdoctoral role delivering research on innovative AI/ML algorithms for predictive modelling and optimisation of agricultural applications from heterogeneous data, as part of an Innovate UK-funded collaborative project. Involves developing AI/ML software tools, data management and analysis, supporting the Principal Investigator on project milestones, and contributing to publications and reports.

Isomorphic Labs · London

Lead the hardening and scaling of an inference platform serving fleets of cutting-edge ML models for scientific applications. Set strategy and roadmaps, architect next-generation services to solve scaling limits, and partner with research, product and operations teams to align infrastructure with biotech R&D cycles.

Harnham - Data and Analytics Recruitment · London · £75,000 – £85,000 pa

Owns the infrastructure, deployment and operational excellence of production machine learning systems within a growing AI and data organisation serving the public sector. Builds ML orchestration pipelines with Dagster, Airflow or Prefect, runs Kubernetes and Terraform/Bicep infrastructure, and maintains CI/CD and observability for models and LLM-powered solutions, working closely with data scientists and client-facing teams.

Salt Search · London · £70,000 – £100,000 pa

Build and scale a cloud-native AI platform for real-time video security, working across Go and Python backend services and React/TypeScript frontends. You'll design APIs and distributed systems handling real-time video and sensor data, collaborating closely with product and ML engineers and owning features from design to production.

Adria Solutions · Huddersfield · £35,000 – £45,000 pa

Supports AWS infrastructure, CI/CD pipelines and automation for software and machine learning workloads, including packaging, versioning and deploying ML models. Works alongside software and AI engineers to keep development, testing and production environments consistent, monitoring pipelines and fixing issues. Suited to an early-career engineer with cloud, scripting and Git foundations and an interest in MLOps.

Ncounter · Manchester · £80,000 – £90,000 pa

Hands-on research engineering role investigating the internal behaviour and security of large language models, turning academic ideas into practical experiments in Python. Work spans model internals, data movement, training risk and safe deployment, alongside production-quality ML engineering, testing and code reviews. Requires active SC clearance and around three days per week on secure sites.