Machine Learning Jobs

• Exeter
• On-site
• £21,805 pa
• Entry level
• Contract

A fully funded PhD studentship researching how small language models can become reliable agents for bounded business workflows, using harness optimisation and fine-tuning on traces from larger teacher models. The work measures reliability, cost, latency and data locality, and releases open tooling for UK SMEs, in collaboration with an industry research lab.

Large Language Models

• 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 sits across AI and telecoms, with training alongside other PhD students in the EPSRC Future Telecommunications Hubs network and exposure to defence, government and infrastructure stakeholders.

AI-on-RAN · 6G

• Exeter
• Entry level
• Contract

A PhD studentship developing neural networks within the MaLCOM machine learning framework to move mixed layer depth prediction from a statistical baseline to temporally resolved hourly forecasts. The researcher selects geographic test sites, temporal scales and oceanographic variables, using high-resolution in-situ and Earth observation data, with training in Python, AI and operational forecasting from Exeter, PML and the Met Office.

MaLCOM

• Norwich
• £20,409 pa
• Entry level
• Contract

Develops and evaluates methods for reliable, localised and generalisable knowledge updating in large language models, adapting techniques such as ROME and MEMIT and comparing them with RAG, external memory and fine-tuning. Experiments use open-weight models on RTX 6000 Ada GPUs and UEA HPC, assessed on benchmarks like CounterFact and zsRE for efficacy, generalisation, locality and retention, with optional deployment in social robots such as Furhat.

ROME · MEMIT · RAG +5

• 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 at the intersection of scientific machine learning and fluid dynamics, using high-fidelity simulations and Imperial's HPC facilities.

HPC

• United Kingdom
• On-site
• Entry level
• Internship · Flexible

A paid 6-12 month PhD internship in the AI & Algorithms department, working across the full robotic learning pipeline from data collection and model training to deployment on real hardware. Interns join mixed-discipline teams on client projects in healthcare and industrial settings, contributing to internal R&D and using labs, robotic platforms and compute resources.

ROS · Git · Isaac Sim +7

• United Kingdom
• Entry level

Work within a mixed-discipline AI & Algorithms team on client and internal R&D projects, building intelligent systems through hands-on coding and training/testing models. You'll attend stand-ups, contribute to planning, and apply state-of-the-art literature to produce market-leading solutions and demos. Requires a PhD and strong Python skills, with a flexible 2026 or 2027 start.

Edge Compute

• London
• On-site
• Entry level
• Permanent

PhD students join an Amazon Science team to own an end-to-end research project, from scoping with a mentor to deploying algorithms, models or systems into production. Projects span computer vision for Prime Video quality, multimodal AI for Rufus, automated reasoning and formal verification, observability for distributed systems, and recommendation models. Interns may publish at top-tier conferences or see their work ship before the internship ends.

Rufus

• Sheffield
• Hybrid
• £39,560 – £41,885 pa
• Entry level
• Contract

Develops explainable machine learning models, including symbolic regression, Bayesian optimisation and genetic algorithms, to decode polymer-solvent thermodynamics and soft-matter physics. Involves featurizing complex polymer architectures, analysing multi-modal experimental datasets, and building open-source cheminformatics tools in Python/Julia for sustainable inverse formulation design.

Python · Julia · C++ +1

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

Lead 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 the Department of Engineering Science, in collaboration with the Oxford Martin AI Governance Initiative, on a two-year fixed-term post.

• Bristol
• On-site
• £54,000 – £58,000 pa
• Mid-level

Develops and evaluates image processing, computer vision and deep learning algorithms for imaging sensor systems, covering segmentation, noise removal, tracking, pose estimation and classification. Works on-site in Bristol within a cross-functional team, supporting research into emerging AI/ML technologies and integrating algorithms into larger operational systems.

Python · C++ · SAR +3

• Bristol

Research, develop and evaluate image processing and computer vision algorithms for complex weapon systems, using imaging sensors for situational awareness and precision targeting. Work spans tracking, optical flow, deep learning and computational imaging, built mainly in Matlab/Simulink and Python with PyTorch, within a collaborative department linked to MBDA sites in France and Germany.

MATLAB · Simulink · Python +1

• Wantage
• On-site
• Senior
• Permanent

Lead the design and deployment of autonomous robotic systems, owning software architecture across perception, planning and control. Work spans prototyping, simulation and field testing, integrating with hardware teams while mentoring engineers and shaping technical roadmaps. Requires depth in C++, ROS2, SLAM and real-time systems.

ROS · ROS2 · Gazebo +1

• New York, United States
• Hybrid
• Lead

Lead the ML approach to manufacturing and MRO data, taking models from design through deployment in customer production environments across cloud, on-prem and edge. Work with sparse, drifting sensor and process data, fusing physics simulation with shop-floor measurement, and set uncertainty quantification and validation standards. Requires travel to customer sites 3-4 weeks per quarter and mentoring of engineers and data scientists.

PyTorch · Pandas · FastAPI +2