Top Machine Learning Jobs in London

Leads a team of TikTok Shop Account Managers while managing strategic brand partnerships. Owns social commerce strategy, GMV and revenue growth, client relationships, executive business reviews, operational playbooks, and cross-functional collaboration. Provides expertise on TikTok Shop features, affiliate ecosystems, creator programs, content optimization, and marketplace trends. Requires extensive ecommerce or marketplace experience, direct people management, advanced analytics, strong leadership, and executive communication skills.

Develop physics-based and data-driven vehicle dynamics models, integrate them into software and simulation workflows, and validate performance against experimental and on-road data. The role includes mathematical modelling, numerical analysis, software optimisation, experimental test planning, performance analysis, and cross-functional collaboration with robotics, simulation, and product teams supporting autonomous driving systems.

Provides client-facing consulting on defensible applications of analytics, machine learning, generative AI, and technology-assisted review in eDiscovery and legal data workflows. Designs, configures, validates, and explains AI-enabled review solutions across platforms such as Relativity and Reveal AI. Advises on workflow strategy, privilege, quality control, statistical validation, and defensibility while managing projects, collaborating cross-functionally, mentoring colleagues, and supporting demonstrations and business development.

Lead machine learning research for molecular foundation models, including architecture design, experimentation, structure generation, spectral retrieval, mass spectrum simulation, and molecular property prediction. Develop molecular and spectral representations, analyze model behavior, establish reproducible evaluation pipelines, and collaborate with computational chemists on leakage-safe benchmarks. Translate research findings into modeling priorities and R&D direction while communicating results to technical and non-technical stakeholders.

Develop Python-based geospatial analyses to quantify carbon removal and reductions for biosphere projects. Build above-ground biomass estimates, dynamic baselines, and uncertainty quantification approaches aligned with validation protocols. Support commercial analysis for prospective project developers, convert bespoke solutions into reusable code, and collaborate with Data Science and Biosphere Science teams. The role requires strong data analysis, geospatial data, cloud-hosted tools, pipeline management, machine learning integration, customer communication, and adaptability in a fast-paced in-person environment.

Leads the algorithm engineering team and partners with leadership to shape Ki’s underwriting algorithm strategy, architecture, roadmap, governance, and engineering culture. The role manages and develops high-performing engineers, guides machine learning product development, advises executives on technology strategy, supports regulatory compliance, and improves the organization’s digital underwriting capabilities.

Sets technical direction for data and AI capabilities, leads large-scale production solutions, establishes engineering standards and reusable frameworks, improves AI monitoring and evaluation, partners with senior stakeholders, mentors engineers, and supports responsible AI adoption across the organization.

Lead AI-driven data and analytics programmes in financial services, mentoring teams and ensuring delivery of measurable outcomes.

The Director of FP&A will own budgeting, rolling forecasts, integrated financial modeling, cash flow and headcount planning, SaaS KPI reporting, variance analysis, board reporting, and PE sponsor reporting. The role will improve planning infrastructure across NetSuite, Salesforce, Pigment, and Bob, apply AI to finance workflows, and lead and develop the FP&A team across a multi-entity, multi-currency SaaS business.

Build and operate production infrastructure for reliable agentic AI systems. Responsibilities include durable agent runtimes, secure tool integration, state and memory management, permissions, guardrails, observability, evaluation, SDKs, workflow frameworks, SLOs, and incident response. The role partners across engineering, security, product, data, and applied science while shaping platform architecture and developer experience.

Designs and operates production GenAI platform infrastructure, including model gateways, routing, caching, evaluation, observability, prompt lifecycle management, guardrails, cost controls, and incident response. Partners with engineering, security, privacy, product, data, and applied science teams to create reliable self-service capabilities for AI workloads.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Develop controllable, realistic adversarial agents for simulation-based autonomous-driving evaluation and deploy them in the Waymo Simulator. Improve post-training recipes for foundation and world models. The internship requires pursuing a PhD in computer science, machine learning, robotics, or a related field, with proficiency in Python, tensor manipulation, machine learning, and deep learning. Experience with reinforcement learning, diffusion or autoregressive architectures, attention mechanisms, autonomous driving, or robotics is preferred.

Design and deploy production-scale AI systems, develop and optimize machine learning and neural network models, work with large language models, refine prompt engineering techniques, and use OpenAI and Anthropic APIs. Collaborate with interdisciplinary teams to build AI applications for the finance sector while keeping current with industry developments.

Manage the full sales cycle across EMEA, including prospecting, pipeline generation, client meetings, contract negotiation, and closing. Build strategic partnerships with mobile gaming and other app-focused clients, exceed revenue quotas, and collaborate with Customer Success, Marketing, and Product. Represent the company at industry events, track mobile advertising trends, and use AI technologies to improve prospecting and sales performance.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Analyze large datasets, apply statistical and machine learning models, perform data analytics, and create visualizations. Collaborate with founders and enterprise clients to solve complex business problems, embedding developed models into the Kepler AI platform. This early-stage role requires hands-on development, strong analytical communication, and comfort working in an ambiguous startup environment.

Own production data and feature pipelines supporting machine learning systems, including training data preparation, feature engineering, model deployment, versioning, retraining, monitoring, and CI/CD. Build batch and ELT pipelines using Python, PySpark, and cloud data platforms. Troubleshoot live systems, make architecture decisions, partner with clients and data scientists, and mentor engineers while delivering solutions with substantial autonomy.

Drive responsible AI adoption across the organisation by identifying automation opportunities, facilitating process-mapping workshops, training colleagues, developing guidance, and prototyping AI-powered solutions. The role builds an AI champions network, manages an opportunity pipeline, establishes governance guardrails, and collaborates with the Data, Systems & GIS team to implement internal tools. It is fully remote with travel for workshops and in-person collaboration.

Own and operate globally distributed GPU infrastructure for model training, evaluation, and serving. Responsibilities include running Kubernetes GPU clusters, optimizing scheduling and placement, repairing unhealthy nodes, managing cloud capacity, and debugging Linux, hardware, storage, and networking failures. The role also involves designing GPU clusters, collaborating with research and product teams, and improving compute reliability and utilization.

Own and operate GPU infrastructure for model training, evaluation, and serving. Responsibilities include running Kubernetes GPU clusters, improving scheduling and placement, repairing unhealthy nodes, managing cloud capacity, and debugging Linux, GPU, storage, and networking failures. The role partners with infrastructure, research, product, and cloud-provider teams to build globally distributed compute systems.

Develop and productionize machine learning models for audience targeting, advertising measurement, and content intelligence. Build scalable batch and real-time serving infrastructure, feature pipelines, APIs, monitoring, CI/CD, and reproducible environments. Establish MLOps engineering standards, reusable templates, and documentation while auditing and migrating existing production models. Partner with data science, MLOps, data engineering, and product teams to deliver reliable, maintainable ML systems.

Build, deploy, and scale production machine learning and deep learning solutions for digital advertising, including audience identity, targeting, and attribution. Develop real-time inference systems, scalable ML pipelines, monitoring, drift detection, and automated retraining on AWS. Collaborate with data engineering and product teams using Spark and Databricks, while mentoring technical professionals and improving model reliability, performance, and cost efficiency.

Build BLP’s core software across infrastructure, backend, machine learning, and product extension platforms. Responsibilities include operating secure and observable cloud infrastructure, developing performant distributed systems, applying AI to business problems, and enabling customer process automation. The role emphasizes formal reasoning, correctness by construction, maintainable systems, type systems, and functional programming. Work is available onsite in Zurich or remotely within Europe.

As a Data Analytics Principal Consultant, you'll lead data transformation initiatives, mentor consultants, and engage with stakeholders in financial services to drive data literacy and innovation.