Top Machine Learning Jobs in London

Lead the execution of production ML systems: build data pipelines, training workflows, evaluation and inference infrastructure, fine-tune large models (LoRA/QLoRA/SFT/DPO), optimize GPU deployments, and integrate ML into products while ensuring robustness, safety, and reliable user-facing performance.

Lead implementation of production-grade ML systems: data pipelines, training/evaluation workflows, scalable GPU inference, model fine-tuning (LoRA/QLoRA/SFT/DPO), deployment, and monitoring. Collaborate with product and application teams to ship reliable, efficient, and safe ML-powered features for real users while managing latency, cost, and robustness constraints.

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.

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

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.

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.

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

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.

Lead development and productionisation of custom ML and LLM systems for customer feedback: fine-tune and evaluate models, build retrieval/reranking and agentic features, design evaluation frameworks, collaborate with Engineering and Product, mentor team members, and drive model-serving and monitoring work.

Build and deploy production-ready AI/ML systems across the full lifecycle, including agentic systems (RAG, prompt engineering), non-GenAI models (recommenders, forecasting), APIs/microservices, CI/CD and IaC, monitoring and guardrails, and cross-functional collaboration to ensure scalable, secure, and observable ML solutions.

Design, build and deploy machine learning and AI solutions end-to-end: data exploration, feature engineering, model selection, evaluation, deployment, maintenance, and tooling to scale ML across products.

Lead technical ML efforts to detect and prevent financial crime by designing, developing and shipping production deep learning, graph and sequence models. Provide IC technical leadership, mentor peers, work with product, engineering and MLOps to iterate models on a cloud-native platform, and measure production impact while minimising false positives and operational costs.

As a Machine Learning Engineer, you will develop production-ready ML solutions, collaborate with multidisciplinary teams, and contribute to Defence and National Security challenges.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.