Artificial Intelligence Jobs

• London
• Hybrid
• Mid-level
• Permanent

Builds and deploys production-grade machine learning systems for national security and AI safety clients, including LLM applications, multi-agent tooling and evaluation of frontier models. Involves technical scoping, architectural decisions, and advising stakeholders, with reusable infrastructure work alongside engineers and data scientists.

AWS · Azure · GCP +5

• Remote (UK)
• Remote
• Mid-level
• Permanent

Deliver bespoke, production-grade machine learning systems for defence clients, covering the full ML lifecycle from architecture and scoping to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, splitting time between client sites and remote work.

Scikit-learn · TensorFlow · PyTorch +5

• Cambridge
• Hybrid · 2 days in office

Conducts independent and collaborative research into language models, neural networks, statistical methods and classical machine learning, prototyping new ideas and improving existing projects. Implements models into the wider software stack, including optimised solutions for edge devices with tight latency and memory constraints, working closely with software engineers and security analysts.

PyTorch · TensorFlow · scikit-learn +2

• London
• On-site
• £150,000 – £175,000 pa
• Senior

Hands-on role building LLM agent and retrieval systems on a small UK core team, reporting to the Head of Engineering. Work spans LangGraph/LangChain workflows, RAG pipelines with Qdrant and GraphRAG on Neo4j, model integration across Anthropic, OpenAI and Bedrock, plus evaluation harnesses and observability. Token cost, latency and auditability are treated as engineering constraints throughout.

LangGraph · LangChain · FastAPI +7

• London
• Senior
• Permanent

Design and implement generative models that learn intervention effects from single-cell perturbation and multiomic data, focusing on counterfactual prediction, generalisation and experimental design. Work in interdisciplinary teams with experimental biologists to validate computational hypotheses and guide the highest-signal next experiments, using modern ML tooling including agentic workflows.

• United Kingdom
• Remote
• Contract

Fellows join an interdisciplinary expert network and are matched to short-term AI/ML projects, ranging from evaluating models and designing experiments to building RL environments and reviewing PyTorch code. They may also co-author technical reports and research papers with Scale's research team. Engagement is fully remote, self-scheduled, with no minimum commitment.

PyTorch · TensorFlow · AWS +2

• 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. Mentor engineers and data scientists while travelling to customer sites roughly 3-4 weeks per quarter.

Python · PyTorch · Pandas +3

• New York, United States
• Hybrid
• US$200,000 – US$250,000 pa
• Senior

Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimization) into customer production environments, building scalable training and inference pipelines and APIs on cloud and on-prem. Leads scoping and architecture design, mentors engineers and data scientists, and travels to customer sites roughly 3-4 weeks per quarter to embed AI models on-site.

Python · PyTorch · Pandas +3

• London
• Hybrid
• Lead · manages people
• Permanent

Lead a small team iterating on Wayve's end-to-end L2 driving model, balancing hands-on Python coding and experiments with directing priorities. You'll root-cause on-road issues and fix them through data recipes, model architecture, evaluation and release readiness, working across teams on model and vehicle interface problems.

Python

• London
• Hybrid
• Senior
• Permanent

Train, debug and improve computer vision and 3D perception models for driver assistance, working across the full ML lifecycle from data and training to evaluation and iteration. The role spans latency-constrained on-car perception and large-scale offline work with lidar, maps and 3D reconstruction to build auto-labelling data flywheels.

LiDAR

• Cambridge
• Hybrid · 2–3 days in office
• Senior

Work on complex R&D projects within a Modelling Team that trains large self-supervised speech models and deploys them to production. Involves distributed training, transformer architectures, and inference optimisation, with mentoring and cross-department collaboration.

GQA · KV-Caching · Flash Attention +2

• London
• Hybrid · 2–3 days in office
• Senior

Join the Modelling Team to train large self-supervised and transformer-based speech models, deploying them into production with strong software engineering and model-serving practices. Work on complex R&D projects, optimise inference, and mentor engineers in a hybrid London setup.

GQA · KV-caching · Flash Attention +2

• Oxford
• £81,322 – £112,488 pa
• Lead

Contract: PermanentLocation: Oxford, hybrid preferred, though we'd consider fully remote for the right person.Reports to: Director of AI DeliveryOxfida (formerly Aioi R&D Lab – Oxford) is an AI R&D company based in Oxford, on a mission to harness AI to…

• Sheffield
• Hybrid
• £32,080 pa
• Entry level
• Contract · Part time

Build and evaluate machine learning models that predict Strep A outbreak risk from multiple real-world datasets, turning them into early-warning signals. Work with clinicians and an engineering team so analyses feed into a practical public health tool, with a focus on reproducible Python and/or R analysis.

• Manchester
• £550 – £600 pd
• Contract

Contract role building and deploying production AI/ML systems, with hands-on work in Python, PyTorch and Google Cloud Platform. The engineer develops Generative AI solutions using transformer architectures, RAG and agent frameworks like ReAct, then packages and serves models via Docker, KServe and Vertex AI endpoints. Responsibilities also cover MLOps practices such as CI/CD pipelines, model registries, monitoring, drift detection and GPU cost optimisation.

GCP · Docker · KServe +3