Top Machine Learning Jobs in London

Lead product marketing for a B2B SaaS productivity platform serving financial services. Own positioning, messaging, sales enablement, competitive intelligence, pricing and packaging, customer communications, and adoption initiatives. Manage and develop one product marketer while partnering closely with Product, Sales, Marketing, and executive leadership. The role is hands-on and offers potential progression into broader marketing leadership.

11-week internship performing quantitative research to develop, backtest, and implement automated trading strategies. Translate mathematical models into code, analyze unconventional data sources, conduct statistical research (ML, time-series, NLP), and collaborate with senior researchers to refine trading signals and monetize models.

The intern will work on developing high-performance research platforms, create tools for trading strategies, and collaborate with teams to innovate financial technology.

Conduct original research applying machine learning, deep learning, and NLP to extract patterns from large and unstructured datasets; implement and back-test algorithms in high-quality code; document findings and collaborate with trading teams to deploy research-driven models.

Build high-performance, production-ready trading and research platforms in small teams. Design systems architectures, develop platforms and web frameworks, apply distributed computing, ML and NLP techniques, and collaborate on complex technical problems.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

Lead and scale Encord’s sales organization, owning revenue growth across enterprise and mid-market markets. Hire, coach, and develop sales talent while personally managing sales cycles during ramp-up. Shape go-to-market strategy, expand ideal customer profiles and verticals, position the AI infrastructure product, engage senior technical buyers, represent the company externally, and collaborate with Product, Engineering, Customer Success, and Marketing.

Lead pharmacovigilance domain expertise for Veeva Falcon by guiding safety case intake, triage, validation, reconciliation, and expedited reporting workflows. Evaluate and calibrate AI-driven outputs, define complex testing scenarios, shape automation strategy, and translate early-adopter feedback into platform improvements. Serve as the bridge between PV operations, technical teams, and executive customers.

Lead discovery and pilot migration activities for Azure ML models and pipelines. Assess dependencies, operational processes, and existing AI platform capabilities; define migration patterns and standards; and lead migration of two models. Coordinate Azure ML, Databricks, and platform engineering teams while contributing hands-on to architecture, engineering, delivery, recommendations, roadmaps, and reusable migration assets.

Build production machine-learning features that transform raw AI usage data into customer-facing insights. Own classification, clustering, data pipelines, model deployment, evaluation frameworks, ground-truth datasets, error analysis, uncertainty quantification, and production reliability. Collaborate with customers, product, engineering, and security teams to shape insight experiences. Work primarily in Python across a polyglot Java, Spring Boot, and TypeScript stack deployed on AWS.

Design, develop, and maintain secure, high-quality production code and system architectures. Troubleshoot complex issues, analyze large datasets for improvements, ensure application resiliency and CI/CD practices, and contribute to engineering communities and inclusive team culture.

Lead technical direction and delivery of large-scale, production ML systems. Design and implement scalable model deployment, lifecycle management, CI/CD, APIs, testing and versioning frameworks. Oversee multiple complex workstreams, justify architectural decisions to stakeholders, coach engineers, and recommend new AI technologies to maintain competitive advantage in high-risk environments.

Leads and develops a team of Data Scientists and other Data professionals, setting strategic direction and identifying high-impact opportunities. Partners with Product, Engineering, and senior stakeholders to shape product strategy and influence decisions through experimentation, causal inference, statistical modelling, machine learning, and analytics. Ensures analytical work drives measurable customer and commercial outcomes while collaborating with other data disciplines on tooling and infrastructure. Also contributes to hiring, coaching, technical standards, and the broader Data Science practice.

Lead a financial crime data science team focused on detecting, preventing, and mitigating financial crime. Partner with Product, Risk, Engineering, and control owners to shape data strategy, generate strategic insights, improve product decisions, and deliver rigorous analytics and machine learning solutions. Hire, develop, and retain data scientists and analysts while supporting innovation through business intelligence, experimentation, causal inference, and self-serve analytics.

Lead the architecture, development, deployment, and scaling of secure, production-grade machine learning systems and AI safety evaluations. The role includes technical scoping, cloud infrastructure design, client advising, cross-functional collaboration, engineering standards, and mentoring. Candidates need strong Python and software engineering expertise, experience with LLM applications, TensorFlow or PyTorch, cloud platforms, cybersecurity, Docker, Kubernetes, and full ML lifecycle operations.

Build and scale AI-powered systems in collaboration with data scientists and product managers. Develop reliable frontend and backend architectures, data pipelines, and cloud infrastructure. Deploy machine learning outputs into production-ready applications, provide technical expertise to customers, and manage the full software lifecycle while balancing rapid prototyping with long-term maintainability.

PhysicsX invites candidates to join its talent community for future opportunities across research, data science, machine learning engineering, product, and delivery. The company develops AI-driven simulation software for engineering and manufacturing in advanced industries including aerospace, energy, semiconductors, materials, and automotive. Applications are reviewed for potential future roles, with no active vacancy or immediate progression guaranteed.

Owns the operational lifecycle of LLM, GenAI, and RAG systems, including deployment, monitoring, evaluation, observability, versioning, cost optimization, and production reliability. The role builds ML platform capabilities for regulated clinical workflows, ships agentic or RAG pipelines, establishes human-feedback evaluation loops, and partners closely with the existing MLOps team.

Defines and measures high-quality conversations for an AI mental health product. The role develops behavioral benchmarks and evaluations, establishes safety constraints, makes evidence-based product decisions, and recruits and coaches distributed human reviewers. It partners with clinical, safety, conversation, and machine learning teams to improve model behavior, maintain reviewer calibration, and evaluate difficult conversational edge cases. The role requires clinical psychology expertise, measurement experience, strong interpersonal skills, and comfort working alongside ML teams.

Perform malware and threat research across the attack lifecycle, validate and improve Falcon detections, collaborate with engineering and ML teams, automate analysis, support external security testing, and translate findings into product protections.

Identify and evaluate audiovisual generative AI technologies, prototype proof-of-concept implementations, test research developments, and build scalable ComfyUI workflows. The role involves model training and inference, dataset management, pipeline integration, hyperparameter tuning, collaboration with research, product, and creative teams, presenting findings, and training artists. It is remote across EMEA.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Supports quantitative trading analysis by analyzing proprietary market data, developing execution signals and impact models, creating visualizations, backtesting trading features, and evaluating algorithm performance. The intern will work with quantitative analysts on execution strategies, market microstructure, statistical analysis, and client-facing research materials. This is a 12-week, New York-based internship requiring advanced Python, pandas, SQL, computational finance, statistics, mathematics, and exposure to machine learning.

Own enterprise sales across the UK and Nordics, managing the full cycle from prospecting and pipeline creation through complex deal closure. Build regional presence through outbound sales, partnerships, events, and networks; engage technical, business, and executive stakeholders; apply MEDDPICC and value-based selling; collaborate with product and engineering; exceed revenue targets and provide accurate forecasts.

Develop and deploy machine learning algorithms that transform multimodal wearable biosignals into scalable health metrics. Own the pipeline from signal processing and model development through evaluation, production integration, and scaling. Fuse data from PPG, motion, temperature, and spectroscopy sensors while addressing motion artifacts and real-world variability. Collaborate with hardware, firmware, software, and data infrastructure teams, and communicate technical findings to senior stakeholders.