Artificial Intelligence Jobs

PhysicsX · London

Builds and owns high-fidelity computational plasma models for semiconductor processes such as etching, CVD/ALD deposition and ion-driven surface interactions, using tools like COMSOL, HPEM, VizGlow and CFD-ACE+. Works directly with semiconductor customers, often on-site, to scope challenges and validate simulations, and collaborates with data scientists and ML engineers to embed the work into AI-powered workflows.

PhysicsX · London

Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, forecasting, anomaly detection) into customer production environments, building scalable training and inference pipelines with Python, PyTorch, Kubeflow and fastAPI. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.

Wayve · London

Owns the autonomy performance measurement framework for a public-scale robotaxi deployment, defining metrics, dashboards and release gates for an end-to-end learned driving system with no existing playbook. Runs the performance improvement loop across model, robot software, backend and evaluation tooling, leading cross-functional execution without direct reporting authority while staying hands-on in Python, C++ and SQL.

Wayve · London

Lead large-scale data enrichment and curation for the Core Model Safety team, mining hard and rare events and building workflows across pre/mid-training, reward modelling and self-supervised labelling. Design and train out-of-distribution detection mechanisms for robust driverless operation, working at the intersection of model engineering, infrastructure, compute and data.

Faculty · London

Deliver bespoke, production-grade machine learning systems for government and national security clients, covering the full ML lifecycle from scoping and architecture to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, while advising stakeholders and defining standards for deploying ML at scale.

Faculty · London

Deliver bespoke, production-grade machine learning systems for government and national security clients, spanning the full ML lifecycle from architecture to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, while advising stakeholders and setting deployment standards.

C4S Search Ltd · London · £600 – £650 pd

Designs, builds and deploys machine learning models into production, with a focus on recommendation systems such as personalised product recommendations, ranking and search optimisation. Works with data scientists, engineers and product stakeholders, maintaining MLOps pipelines for CI/CD, model versioning and monitoring on Azure, and retraining models to sustain accuracy on large-scale datasets.

OpenAI · London

Lead end-to-end deployments of frontier models for strategic customers, owning discovery, scoping, system design, build and production rollout. Build full-stack systems in Python or JavaScript, embed with customer teams and feed field insights back to Research and Product. Requires Spanish fluency and up to 50% travel.

Aioi Nissay Dowa Europe · Oxford · £81,322 – £112,488 pa

This role involves leading research in privacy-preserving generative AI, focusing on techniques like differential privacy, federated learning, and secure inference. The scientist will design and validate machine learning models, translate research into deployable systems, and contribute to publications and collaborations within a multi-partner environment. Emphasis is placed on responsible AI, reproducibility, and mentoring junior team members.

dunnhumby · London

Joining the Retail Insight team, you will investigate, develop and deploy complex models and algorithms for Tesco, covering personalisation, basket analysis, pricing, promotions and loyalty. The role blends statistical modelling, machine learning and client-facing delivery, building reusable, scalable solutions alongside client leadership teams.

Darktrace · Cambridge

Investigates emerging cyber threats, performing in-depth malware analysis and reverse engineering of adversary tactics, techniques and procedures. Produces threat intelligence reports and research publications, develops data-driven threat hunting methodologies and refines detections, collaborating with security researchers and product teams. Hybrid role with at least two days a week in the Cambridge or London office.

Ocado · London

Owns the software engineering that brings data science and machine learning products into live production for the Autoplanner team, building AI-powered planning systems across global warehouses and last-mile assets. Writes typed, well-tested Python (with some Java for OSP integration), builds APIs and data contracts, manages AWS deployments and CI/CD, and packages, serves and monitors ML models. Acts as engineering partner to data scientists and shares on-call support.

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.

Synthesia · Seattle, United States · US$200,000 pa

Builds and operates core enterprise platform systems, ensuring security, reliability and scalability for large customers, and contributes to a new API suite exposing Avatar technology to external creative tools. Owns multi-month, cross-team projects, translating business and regulatory requirements into scalable designs, and works with Python, React and AWS alongside product, security, legal and infrastructure partners.

Causaly · London

Builds and operates the platform running long-lived agentic workflows, where a single run can last hours and fan out across dozens of subagents. Work spans durable execution, checkpointing and retries, LLM integration with guardrails, and tracing and cost attribution across services and two languages, weighted towards backend and platform in TypeScript and Node.js.