Top Machine Learning Jobs in London

Embed with enterprise customers to discover use cases and build production multimodal agent systems (voice, video, text). Architect RAG and MLOps pipelines, deploy and optimize LLM-powered agents, implement observability and evaluation, and integrate agents into customer systems while advising stakeholders and delivering fast POCs.

Design, train, evaluate, and deploy NLP and LLM-based models for document processing, parsing, classification, search, and matching. Own end-to-end ML architecture, run experiments, apply robust development/testing practices, and improve AI tooling and standards.

Depop seeks a Senior Machine Learning Engineer for the Ranking team to develop and maintain machine learning systems for personalized rankings. Responsibilities include designing training pipelines, collaborating with ML scientists, and optimizing model serving systems.

Lead the development and deployment of scalable AI systems, mentor junior engineers, and collaborate with teams to solve client challenges.

The Senior Staff Machine Learning Engineer will define and implement ML strategies for content safety and compliance, build scalable systems, and ensure high-quality content evaluation.

Full-stack AI/ML Software Engineer to build HealthTech SaaS microservices, implement and integrate ML solutions, write high-quality code, author documentation, perform code reviews, and act as an SME on services and systems.

Lead development of a high-performance robotics simulation platform and compiler stack. Design compute infrastructure and dataflows for physics simulation and foundation-model training. Implement JIT/LLVM-based GPU codegen, optimize compile/runtime performance, add differentiable programming support, collaborate with simulation engineers, and contribute to open-source ML compiler projects.

Own and evolve the platform infrastructure for deployed ML and NLP systems: deploy and monitor models via Azure ML and AKS, build orchestration pipelines (Dagster), ensure observability and Responsible AI/GDPR compliance, and support scaling across multi-tenant local authority clients while advising across the organisation.

Lead the Research team in developing scalable machine learning solutions, mentoring junior engineers, optimizing model training, and collaborating on real-world engineering problems.

Lead a multidisciplinary team in machine learning and visual computing, managing timelines, project delivery, and innovation in game rendering technologies.

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.

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.

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

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.

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.

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.

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.

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.

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.