Artificial Intelligence Jobs

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.

Wayve · London

Owns the technical vision and architecture for a platform that lets model engineers deploy, run, evaluate and improve ML models over petabytes of driving data and large GPU fleets. Combines hands-on backend engineering in Python with cross-team technical leadership, mentoring senior engineers and leading a small team of 2-3.

Wayve · London

Lead large-scale training and post-training for Gaia, Wayve's world model that predicts future driving video and runs closed-loop with the driving stack. Own model architecture decisions, push autoregressive rollouts toward longer, more stable generation, and make the model deployment-ready. Partner with research, applications, simulation engineering and cloud teams, mentoring and setting technical direction.

Faculty · London

Set technical direction for complex AI/ML projects, leading delivery of large-scale platforms in high-risk national security environments. Define roadmaps across multiple workstreams, justify architectural decisions to senior stakeholders, and coach engineers while building shared deployment and CI/CD infrastructure. Requires DV eligibility and occasional on-site client work.

NVIDIA · United Kingdom

Develops algorithms and optimizations for NVIDIA's LPX inference and compiler stack, building high-performance runtime and compiler components and mapping large-scale inference workloads onto future hardware. Works at the intersection of compilers, systems and deep learning, using LLVM/MLIR and frameworks like PyTorch and TensorFlow, and collaborates with hardware architects on codesign.

Databricks · London

Serve as the trusted technical ML and AI expert for enterprise customers, architecting production-grade ML and GenAI workloads on the Databricks platform. The role spans RAG architectures, agentic systems, MLOps lifecycle management, and cloud-native integration, while mentoring colleagues and influencing the product roadmap. It combines deep hands-on engineering with customer-facing technical sales support.

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.

Anthropic · London

A four-month, full-time research fellowship working on empirical AI projects in either ML systems/performance or reinforcement learning, using external infrastructure like open-source models and public APIs. Fellows receive direct mentorship from Anthropic researchers and aim to produce a public output such as a paper submission, with projects spanning CPU simulators, accelerator backends, synthetic data pipelines, and RL environments.

Barclays · London

Summer internship in the Client Data & Engineering Group, working on real projects that apply data, analytics and AI to generate client and market insights across the Investment Bank. Interns analyse datasets, build dashboards and visualisations, support data platforms and data quality, and contribute to AI/ML use cases, collaborating with Markets, Banking and Technology stakeholders over a 10-week programme.

PhysicsX · New York, United States · US$90,000 – US$130,000 pa

Customer-facing role owning the full customer journey for physics AI solutions, from onboarding and change management to roadmap planning and contract preparation. Works alongside simulation, software, ML engineers and data scientists, running account rhythms, tracking adoption and building AI-enabled tooling for account operations.

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

Customer-facing role owning the full customer journey, from onboarding and change management to roadmap planning and contract preparation. Works alongside simulation, software, ML engineers and data scientists to translate engineering and physics challenges into product opportunities, tracking engagement and measuring value. Based in Manhattan with hybrid remote flexibility.

DCV Technologies · Manchester · £550 – £600 pd

Contract data science role focused on building and evaluating AI/ML solutions on Google Cloud Platform, including classical machine learning models and LLM-based applications. Day-to-day work spans prompt engineering, RAG concepts, experiment design, model evaluation and documentation, alongside requirements gathering, code review and troubleshooting with project managers and developers.

NES Group Ltd · London

Builds ML and graph-based identity resolution where rule-based matching falls short, improving customer-spine architecture and reducing identity drift. Defines matching-quality metrics, supports golden-layer integration and delivers a reusable entity-resolution framework. Requires record linkage, fuzzy matching, MLOps, feature stores and MDM experience.

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.

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.