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

• 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, from modelling through deployment in customer production environments across cloud, on-prem and edge. Build production ML on sparse, drifting sensor and process data, fusing physics simulation with shop-floor measurement, and set validation and uncertainty-quantification 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, optimisation) into customer production environments, using Python, PyTorch, Pandas, fastAPI, Scipy and Kubeflow across cloud and on-prem. Leads scoping and architecture for data/ML systems, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.

Python · PyTorch · Pandas +3

• London
• Hybrid
• Lead
• Permanent

Lead the technical strategy, architecture and deployment of secure AI systems across high-stakes domains, bridging research and production impact. The role involves capability testing for Frontier AI models, multi-agent systems, scalable cloud infrastructure and safety-first frameworks, while advising senior clients and mentoring engineering talent.

AWS · Azure · GCP +4

• 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
• 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
• 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

• 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.

• Oxford
• Hybrid
• £81,322 – £112,488 pa
• Lead · manages people
• Permanent

Dual role combining line management of a growing ML engineering team with hands-on technical delivery across concurrent projects. You'll shape ML engineering practice, lead multidisciplinary teams, oversee solution design and quality assurance, and support use case discovery and pre-sales alongside commercial teams.

• 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

• Dublin, Ireland
• Hybrid · 2 days in office
• Lead

Lead the architecture, deployment and operation of a large-scale AI/ML platform spanning conventional ML and agentic AI, owning the roadmap and engineering standards. Hands-on role covering MLOps/LLMOps, CI/CD, IaC, Azure infrastructure and multi-agent orchestration frameworks, while mentoring engineers and acting as senior escalation point.

Azure · AWS · Terraform +4