Artificial Intelligence Jobs

• San Francisco, United States
• Hybrid
• US$150,000 – US$190,000 pa

Builds and deploys machine learning pipelines for industrial physics applications, working directly with customers on-site to embed AI models into practical tools. Involves manipulating 3D point-cloud and mesh data, cloud and HPC infrastructure, and translating R&D outputs into reusable libraries and products.

TensorFlow · MLflow · Spark +6

• New York, United States
• Hybrid
• US$150,000 – US$190,000 pa
• Mid-level

Delivery-focused role embedding physics-aware AI models into customer tools, working on-site with clients across aerospace, automotive and energy. Involves building reliable ML data pipelines, manipulating 3D point-cloud and mesh data, and translating R&D outputs into reusable libraries and products. Expects travel of 3-4 weeks per quarter to customer sites worldwide.

TensorFlow · MLflow · Spark +6

• Cambridge
• On-site
• £40,000 – £70,000 pa
• Mid-level
• Permanent

Work across the full machine learning lifecycle, from concept and architecture through data collection, training, integration and testing, with models embedded in in-house electronics to produce prototype and demonstrator devices. Projects span computer vision, generative models and novel methods across multiple domains, in a low-management, collaborative team.

TensorFlow · PyTorch · CUDA +2

• Sydney, Australia
• Hybrid
• Mid-level
• Permanent

Deliver bespoke, production-grade machine learning systems for clients across government, finance, retail and defence. The role spans building and deploying ML software and infrastructure, defining deployment standards, and leading technical scoping and architecture decisions while advising non-technical stakeholders.

Scikit-learn · TensorFlow · PyTorch +5

• London
• Hybrid
• Permanent

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.

Scikit-learn · TensorFlow · PyTorch +5

• Manchester
• Senior

Lead the design and delivery of AI, machine learning, agentic AI and RAG-based solutions for a financial consultancy's client engagements. Build production-grade models with Python and PyTorch, design MLOps frameworks, CI/CD pipelines and cloud-native model serving, while mentoring engineers and shaping technical strategy.

PyTorch · GCP · Docker +4

• London
• Hybrid
• £600 – £650 pd
• Contract

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.

Azure · TensorFlow · PyTorch

• Southampton
• Hybrid
• £41,064 – £45,064 pa
• Contract

Embed physics-informed generative AI tools into a marine vessel concept, generation and evaluation platform as part of a Knowledge Transfer Partnership between Compute Maritime Ltd and the University of Southampton. Develop and validate fast physics-informed models for ship resistance, propulsion and energy efficiency using CFD and benchmark data, and integrate multidisciplinary optimisation methods into the NeuralShipper platform.

PyTorch · TensorFlow · JAX +2

• London
• Hybrid
• Senior
• Permanent

Post-train, distill and evaluate GAIA-family large video diffusion world models for synthetic driving data, covering embodiment and pose transfer. Own large-scale GPU generation pipelines and land synthetic data into the driving-model training stack, working between ML research and platform engineering.

PyTorch · GAIA · CUDA +1

• Brentford
• Hybrid
• £75,000 – £100,000 pa
• Mid-level
• Permanent

Develop an engineering-grade Spiking Neural Network engine for machine vision, turning research outputs into production C++ software. Work alongside researchers, data scientists and engineers on SNNs, CNNs, Transformers, synthetic training data and ISR imagery, with system design, testing and CI/CD responsibilities.

PyTorch · CUDA · CNNs +2

• London
• Remote
• £200,000 – £300,000 pa
• Lead

Own machine learning projects end to end, from experimentation through to production, building reliable, scalable systems with Python and large language models. Provide technical leadership to ML engineers, set engineering standards, lead code reviews and make architecture decisions, working closely with research and application engineering teams to improve model performance in production.

LLM

• 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

• 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

• London
• Hybrid
• £80,000 – £110,000 pa
• Senior

Design, build and deploy production-grade machine learning and generative AI solutions across the full ML lifecycle, from experimentation to deployment, monitoring and optimisation. The role has a strong MLOps and performance focus, improving Python workloads, infrastructure efficiency and model performance while building APIs and working closely with engineering and AI teams.