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

• 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
• 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
• 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

• 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

• 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…

• 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

• 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

• London
• On-site
• Entry level
• Permanent

Builds and maintains scalable training and inference pipelines for biological foundation models such as Transformers and SSMs, optimising latency and throughput. Works with researchers and product engineers to turn notebooks into production systems, owning ML infrastructure like data loading, distributed compute and experiment tracking while prototyping post-training methods.

• London
• On-site
• £150,000 – £200,000 pa
• Mid-level
• Permanent

Develops and fine-tunes robot manipulation policies using behaviour cloning and reinforcement learning, then trains, evaluates and deploys models onto real humanoid hardware. Works within a specialist Manipulation team alongside simulation, teleoperation and controls engineers to improve sim-to-real transfer and training data quality. Distinct for being hands-on with physical robots rather than purely simulation or research.

PyTorch · JAX · OpenVLA +1

• London
• On-site
• Senior
• Permanent

Builds and scales generative and predictive models of cellular behaviour, implementing and optimising large neural networks and distributed training infrastructure. Acts as a bridge between ML research and production, profiling GPU workloads and optimising inference for use by data scientists and biologists on multi-modal biological data.

PyTorch · CUDA · Triton +1