Top Machine Learning Jobs in London

As an ML Engineer, you'll build and deploy scalable ML systems, optimize models, and collaborate with teams to improve content across platforms.

As the Head of Machine Learning Engineering, you will define the ML strategy, lead teams, deliver scalable solutions, and shape AI governance for enhanced customer travel experiences.

Lead architectural design and engineering build-out of company-wide MLOps and agentic AI platforms. Develop and productionize ML/LLM models, RAG, multi-agent systems, and GNNs; implement training, evaluation, serving, feature/vector stores, monitoring, and CI/CD; set engineering standards; collaborate with data science and senior stakeholders; represent company externally.

As a Machine Learning Engineer, you will develop ML models, optimize solutions, automate workflows, and design ML architecture while ensuring high-quality production software delivery.

Lead the development and optimization of AI and machine learning models, mentor junior engineers, and enhance AI-driven features for the platform.

As a Research Engineer, you will develop and optimize AI models for drug discovery, collaborating with scientists to advance ML applications in biopharmaceuticals.

The Machine Learning Researcher will build deep learning models for trading strategies, collaborating with a team to optimize systems and approaches to tackle market challenges.

As an AI Research Engineer at Helsing, you'll architect and enhance deep learning frameworks, optimize infrastructure for distributed training, and develop strategies for large datasets while collaborating with a cross-functional team.

The role involves developing, fine-tuning, and optimizing large language models, building ML infrastructure, and implementing CI/CD practices for AI/ML systems.

Lead technical authority for machine learning systems in the Defence domain. Define architectures, functional and non-functional requirements, solve cross-project technical challenges, guide strategy, mentor engineers, and deliver scalable AI solutions for high-stakes clients requiring UK security eligibility and regular on-site presence.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

As a Lead Machine Learning Engineer, you will set technical direction for ML projects, design scalable systems, mentor engineers, and drive innovation. You'll ensure successful project outcomes while balancing trade-offs and guiding team priorities.

Lead development of ClickHouse's Python connector and SDKs, build integrations with orchestration and transformation tools, drive AI/LLM integration strategy for production data workflows, engage with open-source community, and collaborate across product and platform teams to deliver enterprise-grade data integrations.

Design, build and deploy production-ready AI/ML solutions (including NLP, computer vision and generative AI) for UK public sector clients. Implement MLOps, develop scalable data pipelines and API-driven services, integrate Azure AI/LLM platforms, ensure responsible AI practices, and support proposals, PoCs and knowledge sharing across consulting teams.

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

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Deploy, test, and troubleshoot CHAOS software in customer environments; translate customer workflows into product requirements; design and build new software; integrate sensor data; develop and apply ML/AI solutions; collaborate with engineering teams and travel to customer sites and company offices as needed.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Lead end-to-end ML initiatives: integrate and fine-tune models, optimize GPU inference for latency and throughput, build evaluation and monitoring tooling, collaborate on scalable serving systems, and mentor engineers.

The Senior ML Data Engineer will enhance data workflows, collaborate with teams to build datasets, influence data strategies, and implement pipelines for effective data usage in AI/ML products.

The role involves building end-to-end production-grade ML systems, optimizing pipelines, integrating models into applications, and maintaining data systems.

The VP of Research, Machine Learning will lead the research direction for AI systems, focusing on intelligence design, evaluation frameworks, and advanced machine learning techniques while ensuring real-world applicability.

The Senior Machine Learning Engineer will build core ML systems, handle data preparation and iteration, debug model issues, and ensure reliable production performance while mentoring other engineers.

The Senior Data Engineer designs, maintains, and optimizes ML applications and data pipelines, ensuring compliance and quality while mentoring junior engineers.