Artificial Intelligence Jobs

• London
• Hybrid · 3 days in office
• Senior

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 and Kubeflow. Leads architecture and scoping with customers, mentors engineers, and travels to client sites 3-4 weeks per quarter.

PyTorch · Pandas · FastAPI +2

• Singapore
• Lead

Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimisation) into customer production environments. Leads scoping and architecture for data/ML systems, builds training and inference pipelines and APIs on cloud and on-prem, and mentors engineers while travelling to customer sites 3-4 weeks per quarter.

Python · PyTorch · Pandas +3

• Sydney, Australia
• Hybrid
• Senior
• Permanent

Lead the design, build and deployment of production-grade ML systems for a range of clients, covering architecture, scalable infrastructure and engineering standards. Work across the full ML lifecycle with TensorFlow or PyTorch, cloud platforms and Docker/Kubernetes, while advising customers and mentoring junior engineers.

TensorFlow · PyTorch · AWS +5

• London
• Hybrid
• Senior
• Permanent

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.

PyTorch · CUDA

• London
• Hybrid
• Senior
• Permanent

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.

TensorFlow · PyTorch · AWS +4

• London
• Hybrid
• Senior
• Permanent

Owns the engineering release process for autonomous driving models, defining branching strategies, release cadence, configuration management and quality gates. Reviews model, architecture and code changes against intended outcomes, assesses evaluation results and builds CI/CD tooling and observability to deliver ML systems reliably at scale.

PyTorch · TensorRT · GitHub Actions

• Kraków, Poland
• Hybrid · 2 days in office
• Senior
• Permanent

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.

GCP · Vertex AI · BigQuery +6

• Cambridge
• Hybrid
• Senior

Develops algorithms and optimizations for NVIDIA's LPX inference and compiler stack, building high-performance runtime and compiler components for end-to-end inference. Involves mapping large-scale neural network workloads onto future hardware, working with LLVM/MLIR, and collaborating with hardware architects on codesign. Hybrid role based in Cambridge.

LLVM · MLIR · TensorFlow +3

• Cambridge
• £50,000 – £80,000 pa

Develops intelligent embedded systems using Python, C and C++, applying machine learning and computer vision to real-world products. Works with microcontrollers, sensors and connected hardware, designing software architectures and delivering production code while collaborating directly with clients from concept to finished product.

TensorFlow · TensorFlow Lite · OpenCV +1

• United Kingdom
• Hybrid
• Senior

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.

LLVM · MLIR · TensorFlow +3

• Oxford
• On-site
• £49,119 – £59,955 pa
• Senior · manages people
• Contract

Leads a research strand on Bayesian methods for evaluating and monitoring AI systems, publishing findings, running the lab's open-source software, producing policy-facing outputs, and guiding research assistants and students. Based in the Bayesian Governance Lab within Oxford's Department of Engineering Science, in collaboration with the Oxford Martin AI Governance Initiative, on a two-year fixed-term post.

• Cranfield
• £21,805 pa
• Entry level
• Contract

A PhD studentship researching AI-on-RAN, optimising distributed and mobile inference of large language models by adapting token, memory and bandwidth use in real time. The work spans AI and telecoms, with cross-university FTH training and exposure to defence, government and infrastructure stakeholders, aiming to shape 6G network design.

AI-on-RAN · 6G

• Sheffield
• Hybrid
• £39,560 – £43,099 pa
• Entry level
• Contract

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.

Chatbots · AI-Generated Search Overviews

• Oxford
• On-site
• £40,178 – £48,700 pa
• Entry level
• Contract

Develop and validate machine-learning models on large-scale multimodal NHS clinical data, including electronic health records, genomics, imaging and digital pathology. Build reproducible pipelines within secure data environments and work with clinicians, pathologists and engineers on disease progression and treatment response prediction.

NHS Data · Electronic Health Records · Medical Imaging +3

• London
• £23,805 pa
• Entry level
• Contract

Develop a framework combining a differentiable reacting flow solver with a machine learning-based controller to anticipate and mitigate extreme events such as flashback and thermoacoustic instabilities in hydrogen and SAF combustion. The role sits within the European CONTEXT project, using high-fidelity simulations and Imperial's HPC facilities, with applications spanning novel actuators like spray water injection and electrically-driven droplet control.

HPC