Top Machine Learning Jobs in Edinburgh

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Software engineering intern supporting the Platform Engines group and Policy Engine for SailPoint’s cloud identity security platform. Responsibilities include developing software, procedures, and test solutions; collaborating with software and QA engineers, technical writers, and product managers; and working in Agile teams. The intern will gain exposure to cloud and software technologies including Golang, Angular, AWS, Docker, Kafka, Kubernetes, REST, SQL, and Temporal.

Own the strategy, investment case, governance, and commercialization of Wood Mackenzie’s analytical modeling product portfolio across power, gas, and oil markets. Lead senior product managers, productize research assets, establish validation and documentation standards, and define delivery through applications, APIs, and data platforms. Partner with centralized engineering and modeling teams without directly managing developers. Prioritize portfolio investments, ensure methodological quality, and expand machine-consumable modeling products for trading customers.

Own and improve production machine learning pipelines focused on accuracy, efficiency, and trustworthiness. Build LLM evaluations, judges, datasets, metrics, and benchmarks; run experiments across prompts, models, and pipeline designs; and reduce LLM costs through smaller task-specific models. Expand anonymization capabilities, evaluate open-source models, and collaborate with quality engineers to ship reliable ML features. Document findings and communicate technical trade-offs clearly.

Monitors global oil refineries and petrochemical facilities using real-time infrared imagery, trains machine learning models, queries high-frequency datasets, and generates market insights for trading clients. The role supports process improvement, cross-functional collaboration, client engagement, and integrated analysis of short-term energy markets.

Leads first-line risk and control advice across retail and customer operations, focusing on regulatory, conduct, and operational risk. Modernizes controls through AI, automation, data-led monitoring, preventative design, and reliable evidence. Advises stakeholders on governance, human oversight, explainability, and customer outcomes; identifies control weaknesses, supports change initiatives, reports to governance forums, and coaches colleagues on responsible AI and modern control practices.

Develop, debug, test, and optimize machine learning models, training pipelines, inference systems, numerical methods, and supporting infrastructure. Build reproducible Python workflows, diagnose computational issues, validate implementations, use coding agents, and document technical findings. This remote role requires approximately 15 hours per week and rapid task availability.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Design and deliver enterprise-grade machine learning systems and AI-powered applications for banking. Build scalable, secure, distributed applications and automated cloud, desktop, and ML pipelines. Apply MLOps practices for versioning, reproducibility, and observability while collaborating with cloud and site reliability engineering teams. Develop reusable libraries and business-critical, data-intensive solutions, aligning machine learning problems with business objectives. The role may include mentoring or people leadership.

Serves as a customer-facing fraud and identity subject matter expert across pre-sales, onboarding, and expansion. Advises enterprise customers on risk profiles, fraud strategies, identity workflows, integrations, regulatory hurdles, and implementation best practices. Leads onboarding and go-live readiness, coordinates cross-functional teams, validates milestones, and helps customers optimize verification workflows, reduce friction, accelerate deployment, and maximize product value.

Lead a small team developing secure, reliable agentic AI capabilities. Responsibilities include setting technical direction, building agent monitoring, evaluation, and guardrail frameworks, performing machine learning tasks, translating research into production features, mentoring engineers, and contributing hands-on code and technical designs. The role focuses on agent collaboration, trust, security, quality measurement, and scalable software delivery.

Develop data products and AI solutions using statistical modeling, machine learning, generative AI, and cloud platforms. The role involves coding in Python and SQL, analyzing large datasets, translating business needs into solutions, collaborating with stakeholders, and promoting responsible AI adoption across Co-op business areas.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Leads end-to-end AI-powered insight and market research programmes for major client accounts. Responsibilities include shaping briefs, advising clients, managing scopes, budgets, timelines and resources, supporting pitches, leading consultants, and improving research delivery through AI-enabled tools. The role requires commercial research expertise, strong client leadership, organizational skills, and curiosity about emerging AI technologies.

Own the vision, strategy, roadmap, integration, commercialization, and executive stewardship of Kaltura’s new AI-powered learning and enablement platform. Lead 0-to-1 MVP delivery and post-MVP expansion across AI course generation, conversational tutoring, multimodal learning, simulations, and personalized journeys. Partner with engineering and GTM teams on architecture, pricing, packaging, positioning, enterprise sales, and integration of acquired products.

Conduct organic chemistry synthesis research under the King-Smith Group to advance AI-guided chemistry. The role involves completing research projects, applying machine learning concepts to synthesis, communicating findings effectively, and contributing to a collaborative academic research environment. The position is full-time, fixed-term for 24 months, with part-time and flexible working arrangements potentially available.

Lead the vision, strategy, roadmap, and lifecycle of data, analytics, and AI-enabled products. Identify commercial opportunities, develop pricing and business cases, manage budgets, and drive product profitability. Partner with Product, Data Science, Engineering, and Commercial teams to deliver scalable solutions. Lead and develop Data Product Managers while influencing senior stakeholders and translating technical capabilities into customer and business value.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Leads economists, econometricians, and data analysts delivering applied economic, econometric, machine learning, and policy analysis for communications regulation. Responsibilities include directing concurrent projects, evaluating policies, shaping regulatory decisions, communicating findings to stakeholders, producing publications, and mentoring colleagues. The role requires extensive real-world economic analysis experience, strong applied microeconomics and econometrics expertise, analytical software proficiency, and the ability to lead teams and develop innovative quantitative approaches.

Build and support secure full-stack web applications, internal tools, APIs, data pipelines, and database schemas. Develop AI agents and LLM-powered solutions using Agentic AI, RAG, tool calling, guardrails, and Microsoft Foundry. Deploy machine learning models for predictive analytics, implement automated testing and CI/CD, resolve application and data issues, and modernize legacy systems. Collaborate with technical teams and business stakeholders to deliver scalable information and decision-support solutions.

Provides statistical leadership for Medical Affairs, real-world evidence, comparative effectiveness, HTA, and HEOR activities. Designs and analyzes observational, clinical, Phase IV, and indirect treatment comparison studies; evaluates data quality, bias, uncertainty, and applicability; supports evidence-generation strategies, reimbursement submissions, publications, and scientific communications. Collaborates with statistics, epidemiology, RWE, medical, and external scientific teams to translate complex analyses into actionable conclusions.

The Principal Solutions Architect leads technical solution design, partners with hyperscalers, ensures architectural integrity, and drives customer engagement through compelling narratives and demos.

Build and own pricing data science capabilities, including demand and elasticity models, forecasting, willingness-to-pay models, pricing optimization, experimentation, and causal analysis. Deploy and monitor machine learning systems, establish governed pricing metrics, and partner with Commercial, Finance, and Product to improve margin and revenue. Set analytical standards for pricing decisions and build reusable analytical capabilities.

Conduct theoretical particle physics research on LHC phenomenology within the Higgs Centre for Theoretical Physics. Work will focus on extracting Standard Model parameters, developing machine learning tools, and calculating approximate N3LO corrections to top and jet cross-sections, with opportunities to collaborate with the NNPDF collaboration. The role also requires independent academic research, statistical data analysis, and communicating results through peer-reviewed publications and conference presentations.