Top Machine Learning Jobs in London

Provides first-line technical support to global SaaS customers through phone, email, and other channels. Diagnoses and resolves product issues, documents cases, manages escalations, collaborates with Engineering, Product, and Customer Success, and identifies recurring problems. The role also contributes to technical documentation, knowledge-base content, customer training materials, and product improvement while working a shift-based support rota.

Graduate machine learning engineers will work with quantitative researchers on advanced machine learning and scientific computing problems. Responsibilities include implementing published research, developing custom libraries for large-scale model training, optimizing machine learning operations, profiling architectures, identifying performance bottlenecks, and evaluating ML hardware and software. Candidates should have strong object-oriented programming and machine learning skills, with experience in Python, PyTorch, NumPy, optimization, HPC, profiling, or model inference.

Lead and mentor a data science team developing machine learning systems for threat detection and agentic AI. Set ML technical strategy, standards, roadmaps, and deployment practices; guide projects from research through production; collaborate with security, engineering, and product teams; develop advanced malware and behavioral threat models; communicate insights; and contribute to research, publications, patents, and hands-on technical work.

Embed with strategic customers to design, build, and implement data-driven AI solutions that improve customer support automation and Fin adoption. Analyze workflows and business challenges, establish measurement best practices, prototype and scale AI tools, and collaborate with Sales, Success, Product, and Engineering. Feed deployment insights back into product strategy while translating complex technical analysis into measurable business outcomes.

Drive enterprise revenue for Provenir’s Agentic AI Platform, initially expanding within existing clients and then acquiring new-logo customers. Build expertise in fraud and financial crime, lead complex 6–12+ month sales cycles, develop value-based business cases, engage C-level stakeholders, coordinate cross-functional teams, negotiate multi-year agreements, and maintain accurate pipeline forecasting. The role targets regulated financial services organizations and high-value contracts of approximately $1M+ annually.

Leads the strategic vision, lifecycle, roadmap, governance, and commercial execution of enterprise decisioning platforms. Responsibilities include developing automated decision engines and workflow tools, integrating cloud-native architecture and AI, conducting market and customer research, managing product performance and revenue, and ensuring regulatory compliance. The role requires experience with B2B SaaS, risk or credit decisioning, automated workflows, cloud platforms, APIs, and AI-enabled products.

Translate ambiguous business questions into actionable insights using Python, SQL, statistics, forecasting, regression, classification, segmentation, and machine learning. Analyze complex datasets, validate findings, build models, and communicate commercial implications to senior stakeholders. The role requires strong requirements gathering, stakeholder management, written communication, and the ability to turn analysis into practical decisions in a financial services environment.

The role involves designing scalable data pipelines, implementing custom ML algorithms, and working on microservice architectures for ad optimization models.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

Serve as the technical expert for industrial AI sales, building relationships with cement plant engineers and leaders. Analyze plant data, identify opportunities, lead technical discovery, create commercial proposals, handle objections, and ensure successful handoffs to deployment teams. Provide field feedback to product and machine learning teams, improve the pre-sales process, and travel to plants and conferences approximately one week per month.

Lead research and development of Transformer-based and time-series foundation models for systematic trading. Responsibilities include pre-training models from scratch on large-scale financial datasets, designing representations and self-supervised objectives, developing distributed training systems, fine-tuning models for trading applications, studying scaling and regime robustness, and evaluating economic performance through simulations and live-trading metrics.

Analyze complex datasets, develop dashboards and reports, translate stakeholder requirements into data-driven solutions, and support analytical models and AI-enabled capabilities. The role involves collaborating with engineers and multidisciplinary Agile teams, ensuring data quality and documentation, presenting insights, and supporting UX/UI design within a secure Defence programme.

Own post-booking product areas for US credit cards, including payments, collections, credit limit increases, spending, and the mobile app. Run customer experiments, conduct research, collaborate with credit risk and data science, prototype solutions with AI, build automations, and launch improvements. The role requires comfort with ambiguity, data-driven decision-making, rapid testing, and hands-on execution across product, engineering, operations, legal, and compliance.

Own post-booking credit card product areas including payments, collections, credit limit increases, spending, and the mobile app. Run customer experiments, conduct research, use data and AI to test opportunities, collaborate with credit risk and data science, build automations, and deliver solutions with engineering, operations, legal, and compliance teams.

Leads a multinational BDR organization of approximately 18 representatives through team leaders. Owns BDR strategy, playbook development, performance management, metrics, sales technology adoption, process optimization, and cross-functional alignment with Sales, Marketing, and senior leadership. The role combines strategic planning with hands-on operational leadership across European and US markets, driving scalable prospecting, qualification, conversion, and team development.

Build and improve production AI systems including chatbots, document extraction pipelines, LLM workflows, and voice AI. Responsibilities span discovery, prototyping, Python development, deployment, evaluation, monitoring, and process automation. The role requires applying LLMs, retrieval systems, prompts, and agent workflows to simplify financial-services operations and autonomously complete customer account actions.

Build and improve production AI systems for fintech operations, including chatbots, document extraction pipelines, LLM workflows, and autonomous account actions. Responsibilities span opportunity discovery, rapid prototyping, prompt and retrieval optimization, deployment, evaluation, monitoring, and process automation. The role requires strong Python engineering, practical LLM experience, sound judgment in ambiguous situations, and the ability to communicate trade-offs and influence stakeholders.

Develop and deploy advanced machine learning models for speech recognition, including large self-supervised and transformer-based systems. Lead complex R&D projects, apply distributed training and inference optimization techniques, collaborate across departments, stay current with ML developments, and mentor team members. The role combines cutting-edge research with production model serving and software engineering best practices.

Lead technical direction and delivery of large-scale AI/ML platforms. Architect microservices and shared libraries, align ML, data science and DevOps, coach distributed engineers, define production readiness, and drive hiring and standards across software engineering.

Own and grow enterprise sales for Algolia’s AI-powered search and discovery platform. Manage complex, multi-stakeholder sales cycles, build territory and account strategies, generate new opportunities, expand existing accounts, and partner with Solutions Engineering on demos, proofs of value, and ROI analyses. Forecast using MEDDPICC and collaborate with SDRs, Customer Success, Legal, Marketing, and partners to close high-value deals with Global 2000 organizations.

Deliver and deploy ML and generative AI solutions across industries: design experiments, build production-scale models, identify business use cases, drive measurable value, consult with clients, and mentor team members.

Build and deploy production-grade machine learning software, tools, and infrastructure for high-stakes AI safety and national security applications. The role involves creating scalable ML solutions, evaluating frontier AI models, making architectural decisions, advising clients, and collaborating with engineers, data scientists, commercial teams, and research labs. Candidates should have strong Python and software engineering skills, cloud and containerization experience, ML lifecycle knowledge, and familiarity with LLM applications and AI safety evaluation.

Join iSAM Vector as a Quantitative Researcher to design, test and implement systematic trading signals across global markets. Responsibilities include signal research, backtesting, model refinement, dataset and tool development, portfolio construction support, and communicating results to technical and non-technical stakeholders.

Ten-week internship combining financial research, client-facing support, and software engineering. Responsibilities include debugging Python, developing REST APIs and data pipelines, building Python libraries, deploying infrastructure and production code, monitoring systems, testing bespoke client solutions, supporting research functionality, clarifying requirements, and presenting product progress to stakeholders. The role requires independent problem-solving, cross-functional communication, and an entrepreneurial approach in a fast-paced capital-markets technology startup.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.