Top Machine Learning Jobs in London

Lead deployment of production ML systems for physics-based engineering: design scalable training/inference pipelines, work with 3D point-clouds and mesh data, collaborate with customers onsite, mentor engineers, define architecture and success metrics, and deliver robust cloud and on-prem ML solutions.

The Staff Machine Learning Engineer will lead the execution of ML systems, ensuring reliability and scalability while collaborating with cross-functional teams to deliver high-quality AI solutions.

As Technical Lead, you'll own ML system execution, manage data pipelines, model deployment, and ensure performance under real-world conditions while collaborating with the team.

Deliver production-ready ML model releases: train and iterate PyTorch models, evaluate and debug performance, apply optimizations (quantisation, distillation), collaborate with performance and deployment teams, and ensure models meet tight on-vehicle runtime constraints.

Optimize transformer-based ML inference for edge accelerators and GPUs. Implement and validate compiler, runtime, and kernel optimizations; profile end-to-end inference stacks; build benchmarking and regression tests; target multiple platforms (NVIDIA, Qualcomm); collaborate with model developers and cross-functional teams to deliver performant, maintainable on-vehicle ML systems and contribute to technical roadmaps.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead and own ML platform end-to-end: data/feature pipelines, training and inference infrastructure, model registry, deployment, monitoring, and agentic AI stack. Design integrations across teams and third parties, ensure reproducibility, reliability, cost control, governance and audit trails, and grow a small engineering function supporting data scientists.

Design and own machine learning platforms for pricing technology, streamline workflows, and ensure reliable model performance at scale.

Build and deploy production-grade ML software, tools and infrastructure; create reusable, scalable ML solutions; collaborate with engineers, data scientists and commercial leads; lead technical scoping and architecture; define deployment standards; advise clients and stakeholders on ML feasibility and delivery.

Lead design, quantization, and deployment of edge-native SLMs and knowledge analytics to air-gapped, bandwidth-constrained tactical hardware. Ensure JSP 936 Dependable AI compliance, build on-device graph/datapipelines, reproducible MLOps for low-bandwidth environments, and advise MoD stakeholders.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

As a Staff/Principal Machine Learning Engineer, you will optimize and scale multimodal inference systems, ensuring high performance and reliability in production environments.

As a Lead ML Ops Engineer, you will define standards for ML delivery and operations, shape ML systems engineering, and influence practices across teams.

Develop the ML compiler stack for AI acceleration hardware and optimize performance for large language models in a collaborative environment.

The Senior ML Runtime Engineer will integrate AI acceleration hardware with open source projects, develop high-performance Rust runtime, and collaborate on hardware-software design.

The role involves developing and optimizing AI models, conducting research, and innovating new features for generative AI tools, enhancing creative user experiences.

The role requires developing ML and quantitative systems using financial time series data, deploying models, and improving signal evaluation for investors.

Design, prototype, and implement ML algorithms to maximize advertising ROI. Write production code, test models on historical data, iterate on solutions, and collaborate with data engineers to deploy scalable pipelines.

Develop and ship features for Junie across CLI and model integrations, experiment with model setups and ensembling for cost-quality tradeoffs, build and test autonomous agent behaviors for long-term maintenance, and own projects from research to production while using Junie daily to iterate on quality and UX.

Lead technical development and production deployment of advanced ML solutions (including LLMs, RAG, graph and sequence models) to improve customer operations. Provide hands-on engineering, mentor ML engineers, collaborate with product and backend teams, and drive MLOps and lifecycle strategy to route, disambiguate and resolve customer queries at scale.

Turn research prototypes into scalable, fault-tolerant ML and physics infrastructure for drug discovery. Improve GPU utilization, distributed execution, reproducibility, orchestration, monitoring, and packaging so scientists can run reliable, multi-cluster workflows and agent-usable pipelines.

Lead R&D to design and deploy generative models for graphics and video processing. Research and prototype novel methods, build data generation pipelines, collaborate on publications and patents, and support cross-team integration for game rendering and streaming applications.

Design, build, and productionize end-to-end ML and LLM-powered recommendation features: architect data pipelines for massive datasets, train and serve models, integrate real-time stores and APIs, set up monitoring and A/B testing, debug distributed systems, and collaborate with Product, Design, and Engineering to deliver scalable personalization.

Internship working on real ML projects with full-time mentors: train models, develop or improve ML libraries and workflows, use large GPU clusters, and apply ML to noisy financial data while learning production ML engineering practices.

Work alongside full-time ML researchers on end-to-end projects using large market datasets and massive compute. Tasks include model design, implementation in Python and ML frameworks, hyperparameter tuning, debugging training issues, and analyzing model predictions for systematic trading challenges in nonstationary multi-agent environments.