Top Machine Learning Jobs in London

Lead strategic presales and value engineering for AI and data transformation opportunities. Facilitate executive discovery, quantify ROI and business value, shape solution hypotheses, create AI prototypes and demos, and support proposals, pricing, and deal closure. Partner with sales, delivery, architecture, marketing, and leadership to ensure solutions are compelling and feasible. Contribute thought leadership, reusable presales assets, client workshops, and market positioning around GenAI, agentic workflows, modern data platforms, and enterprise transformation.

Serve as a customer-facing technical advisor for Snowflake, partnering with sales teams and channel partners throughout the sales cycle. Present solutions to technical and executive audiences, conduct demos and proofs of concept, design architectures, develop technical champions, and solve data-related business problems. Collaborate with Product, Engineering, Marketing, and implementation partners while staying current on Snowflake, cloud, data, and AI technologies.

Partners with sales teams and customers to identify needs, lead winning sales cycles, deliver executive and technical presentations, demonstrate Snowflake solutions, support enterprise proofs of concept, and help close large deals. The role requires expertise in data platforms, cloud technologies, generative AI, machine learning, LLM ecosystems, data governance, data modeling, and data engineering, while collaborating with Product, Engineering, Marketing, and channel partners.

Designs enterprise-grade cloud, data, and AI solutions for airline operations. Responsibilities include assessing technology landscapes, identifying modernization and integration opportunities, translating business priorities into technical architectures, creating solution blueprints and documentation, promoting enterprise architecture best practices, and guiding customer teams through transformation initiatives.

Research, develop, and refine predictive signals and quantitative models for systematic investment strategies. Responsibilities include sourcing novel datasets, statistical modeling, machine learning, large-scale data analysis, designing and validating trading signals, backtesting hypotheses across market conditions, and collaborating with portfolio managers to deploy successful ideas.

Design, build, deploy, monitor, and optimize production machine learning systems for personalization, recommendations, product ranking, and AI-powered styling. Develop batch and real-time models at scale, collaborate with applied scientists, software engineers, and product partners, and contribute to MLOps tooling, engineering best practices, and shared machine learning platforms.

Supports ML infrastructure for autonomous driving by maintaining large multimodal datasets and feature data, improving workflows, and identifying user and agent use cases. The intern develops systems for machine learning training and inference, using Python or C++ and distributed systems. Preferred experience includes feature extraction, distributed storage, TensorFlow, JAX, deep learning, autonomous driving, or robotics. The role is hybrid and requires current Master's or PhD enrollment.

Architect, develop, and operate scalable Java backend services supporting subscription conversion, retention, upsells, and personalized user journeys. Design cloud-native systems, data models, and APIs on Google Cloud Platform; collaborate with product, marketing, growth, and engineering teams; mentor colleagues; and lead complex platform initiatives involving automation, content management, machine learning, and high-volume distributed services.

Lead end-to-end machine learning systems across data, training, fine-tuning, evaluation, inference, deployment, and monitoring. Build scalable GPU-based infrastructure and reliable production pipelines for large models, optimize performance and cost, and partner with research and application engineering to translate model capabilities into product improvements. Provide hands-on technical leadership, resolve production issues, and ensure measurable, safe, and maintainable ML iterations.

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.

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

Develop, fine-tune, and operationalize machine learning models and AI applications for enterprise customers. Build demos and proofs of concept, integrate new models, enhance internal ML platform features, resolve bugs, optimize performance and scalability, and support partner solutions. Collaborate with sales and solutions teams to ensure successful customer deployments. The role requires strong software engineering, coding, communication, and project execution skills, with preferred experience in generative AI model fine-tuning.

Develop and productionize machine learning models and AI agents for financial-market intelligence and client workflows. Responsibilities include forecasting, sentiment analysis, market classification, LLM/RAG agent development, backtesting, hallucination testing, governance, human-review controls, provider benchmarking, regulatory compliance, and cross-functional collaboration. The role also provides technical leadership and mentorship while applying advances in AI and machine learning to financial markets.

Conduct machine learning research for autonomous-driving simulation realism. Responsibilities include contributing to existing projects, implementing research ideas, and running training and evaluation experiments. The role requires Python programming and deep learning experience with TensorFlow, JAX, or PyTorch. Preferred experience includes attention mechanisms, autoregressive architectures, diffusion for multimodal data, autonomous driving, and robotics.

Lead technically complex product work on Rosso, embedding with ML, data science and engineering teams to turn business and customer needs into shipped features. Define success criteria for models/forecasts, manage edge cases and operational risk, and prioritize work across portfolio, forecasting, pricing optimisation and related systems.

Conduct end-to-end ML research for options trading: design experiments, train large-scale models (LLMs/transformers), build scalable pipelines, validate and deploy models to production, and translate research into measurable P&L impact.

Lead enterprise-wide AI transformation by defining strategy, operating model, governance, and roadmap. Build and run an AI Transformation Office, scale high-impact AI use cases, ensure responsible AI governance, drive change management and AI literacy, measure ROI and automation maturity, and align cross-functional teams to deliver measurable business value.

Lead the design, development, and deployment of scalable, production-grade machine learning systems. Establish ML engineering standards, make architectural decisions, collaborate with technical and commercial stakeholders, advise clients, and mentor junior engineers. The role requires expertise across the ML lifecycle, cloud infrastructure, software engineering, containers, orchestration, and responsible AI delivery.

Conduct research on real-world BGP usage and Internet routing behavior using large-scale data, statistics, data science, and AI/ML techniques. Analyze routing dynamics and Internet topology, prototype algorithms and tools, collaborate with researchers, engineers, and product teams, and contribute to production systems, technical reports, and research publications.

Build scalable performance tooling for AI model training and inference across cloud and embedded hardware. Responsibilities include profiling workloads, conducting roofline analysis, identifying layer- and operator-level bottlenecks, predicting latency, memory, utilization, and compute costs, and monitoring regressions. The role requires close collaboration with model, compiler, runtime, platform, and hardware teams to establish performance targets and translate measurements into actionable recommendations.

Own and extend Wayve’s machine learning compilation pipeline from model checkpoint to deployable embedded bundles across NVIDIA TensorRT and Qualcomm QNN targets. Design compiler passes, quantization and precision-typing systems, graph partitioning, legalization, regression testing, and benchmarking infrastructure. Partner with model and training teams to ensure compilability, accuracy, and latency across architectures and SoCs. Provide technical direction and mentorship for compiler engineering.

Designs, builds, and maintains AWS and on-premises infrastructure for machine learning engineers, data scientists, and software teams. Responsibilities include operating Kubernetes and MLOps platforms, responding to service requests, troubleshooting production issues, implementing infrastructure as code, improving reliability and developer experience, and supporting secure, scalable product delivery. The role requires cross-team collaboration, clear technical communication, and commuting to London two days per week.

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.