Top Machine Learning Jobs in London

As a Reinforcement Learning Engineer, you will own production trading systems, design reward functions, validate frameworks, and lead RL efforts to drive trading volume safely with real capital.

Lead Liberis' credit risk strategy and appetite, manage portfolio performance across products and partners, and oversee Portfolio Management & Analytics, Underwriting, and Collections & Recoveries. Partner with Commercial, Finance, Pricing, Decisioning, ML, Product and Operations to enable growth while controlling risk-adjusted returns. Build and retain high-performing teams, establish governance and decision frameworks, contribute to new products and market expansion, and provide actionable risk insights to senior leadership.

As a Senior Machine Learning Scientist, you will identify ML opportunities, design algorithms, conduct analyses, and collaborate with product teams to build effective ML solutions for customer service enhancement.

As a Staff Machine Learning Scientist, you will focus on hiring and mentoring staff, raising technical standards, and applying machine learning to improve customer experiences. You'll research algorithms, conduct data analysis, and collaborate with teams to produce ML products.

Design and develop novel machine learning models and training algorithms for drug discovery. Lead and run research projects, implement and scale training/inference frameworks, analyze experiments, and collaborate across disciplines. Mentor and provide technical guidance to other ML researchers and contribute to research direction and presentations.

The Director of Engineering will lead the Simulation Team in London, overseeing technical roadmap, project management, team recruitment, and collaborations with DeepMind for generative model advancements.

Design and develop image/video diffusion models, perform large-scale model training, maintain data for training, and optimize models for production use.

The Treasury Manager will oversee treasury operations, manage cash flow, implement risk management practices, and lead a high-performing team while aligning strategies with business objectives.

Drive strategic revenue and transform enterprise client relationships through long-term engagements, navigating challenges and delivering measurable business value.

Join the Data Science team to innovate solutions for data problems, develop forecasting models, and enhance quant strategies using machine learning.

Maintain, validate, and troubleshoot production Machine Vision and Machine Learning inspection applications. Execute testing, simulation, and cybersecurity patch validation; develop automation and deployment tools; implement software modifications; collect and analyse operational data; produce test documentation and reports; and collaborate with IT Security, infrastructure, and global ML/inspection teams to sustain performance and drive continuous improvement.

Develop and deploy applied machine learning solutions using large language models for enterprise customers. Lead projects from ideation through production, align engineering and product teams, mentor distributed engineers, identify model gaps, define technical direction, collaborate with partners, and support patent preparation.

Build and scale a predictive modeling platform supporting multiple problem types and tenants. Develop embedding-based, temporal survival, and gradient-boosted models for social-domain risk prediction. Create training datasets, evaluation pipelines, and production ML infrastructure, while researching and safely deploying emerging ML and LLM techniques. Implement containerized APIs and Kubernetes services, and incorporate responsible AI, ethical, and regulatory considerations throughout development.

Develop and apply sequence‑modelling ML techniques to DNA to interpret variants, discover genes, and model regulation. Train, fine‑tune and evaluate models, collaborate with computational and experimental teams, leverage large-scale datasets, design robust biological evaluations, and integrate ML advances into translational target discovery.

Design and optimize overseas risk-control strategy and tools; manage anti-fraud and chargeback countermeasures; perform user/merchant data analysis; develop risk indicators, monitor daily risks, and produce reports to guide process optimization and business decisions.

Lead design and operation of end-to-end risk governance and decision systems for marketplace fraud and integrity. Build real-time and pre-transaction controls, integrate rule engines with ML/AI signals, set decision policies, tune production systems, and lead cross-functional teams to detect, prevent, and optimize fraud prevention at scale.

Lead and mentor an algorithms team to design, develop, and optimize search and recommendation models. Oversee traffic distribution, experimentation, scalable deployment, and performance evaluation to improve personalization, conversion, and engagement while collaborating with product, engineering, and business teams.

The role involves building and maintaining ML and cloud infrastructure for training large-scale generative models. Responsibilities include designing GPU clusters, supporting distributed training, managing storage systems, and collaborating with researchers and engineers.

Design and prototype generative ML models for protein design, build and maintain ML data pipelines and infrastructure, optimize training/inference on cloud accelerators, collaborate with protein engineers and biologists to plan wet-lab testing and iterate models based on experimental results, and share research externally.

Lead and execute large-scale training runs for video/foundation models, contribute to architecture and training strategy, improve world-model capabilities for synthetic scenario generation, partner across teams to deliver end-to-end solutions, and provide technical leadership and mentorship.

Lead organisational ML engineering strategy and architecture for high-stakes programmes. Own end-to-end delivery, drive adoption of platforms and technologies, coach senior engineering talent, shape executive client engagements, and lead technical pre-sales to win flagship AI projects.

As a Data & AI/ML Architect, you will conduct workshops, oversee project delivery, design architectures, and mentor juniors while engaging with clients.

Provides principal-level technical leadership for enterprise machine learning systems supporting a matching platform. Responsibilities include ML architecture, feature pipelines, model development, AWS SageMaker MLOps, low-latency inference, explainability, governance, security, monitoring, drift detection, testing, and deployment automation. The role mentors engineers, establishes engineering standards, and drives scalable, reliable, compliant ML platform capabilities across environments.

Optimize large-scale machine learning workloads across GPU and CPU infrastructure. Profile, benchmark, and tune distributed training and inference jobs; eliminate bottlenecks; develop reference implementations, libraries, and performance tools; and collaborate with research, infrastructure, architecture, and platform teams to improve compute systems and guide long-term platform decisions.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.