Top Machine Learning Jobs in Edinburgh

Lead and scale engineering teams building multi-tenant Voice AI, Agent QA, analytics, auto-learning, and workforce management platforms. Own architecture for high-throughput voice data systems, real-time pipelines, telephony integrations, optimization algorithms, and enterprise compliance. Recruit and develop senior engineers and managers, establish engineering standards, guide cross-functional roadmap alignment, and deliver reliable production systems across global geographies.

Own technical product marketing for Bloomreach’s APIs, integrations, and Loomi agentic AI platform. Develop positioning, use cases, partner launch strategies, go-to-market plans, customer messaging, sales enablement, and technical integration materials. Collaborate cross-functionally with Product, Engineering, Solutions Engineering, partners, sales, and enterprise stakeholders to translate complex platform capabilities into compelling customer value. Define launch metrics, evaluate outcomes, and communicate AI and data technologies clearly to technical audiences.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Own the strategy, requirements, evaluation, and reliability of AI agent products. Partner with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, UX, and reliability. Define offline and online evaluation frameworks, incorporate human feedback, guide prioritization, and ensure AI features are understandable, predictable, safe, and trusted by users.

Own the end-to-end product strategy and execution for AI agent systems. Define technical and behavioral requirements, evaluate model performance, guide trade-offs across quality, latency, cost, reliability, and user experience, and partner closely with ML, backend, and mobile engineers. Establish evaluation frameworks, prioritize roadmaps, drive rapid and safe delivery, and ensure AI features are predictable, trustworthy, and valuable to users.

Lead platform product ownership bridging cloud and firmware strategy and execution. Manage and prioritize backlog, engage customers, collaborate with R&D, define releases, and drive agile delivery aligned with product leadership.

Own the Device Intelligence product roadmap for tier‑1 network service providers. Define PRDs, AI/LLM-assisted workflows, and technical requirements; lead discovery, backlog prioritization, and cross-functional delivery; engage strategic customers and support sales with competitive positioning.

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

Lead Rasa’s product organization and define strategy for its enterprise conversational AI platform. Manage and develop a team of product managers, own product roadmaps and lifecycle decisions, and partner with engineering, sales, and marketing on product definition, pricing, packaging, and go-to-market. Use customer research, market analysis, competitive intelligence, and developer feedback to guide innovation and foster a strong ecosystem across cloud, on-premises, and open-source deployments.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

The role involves building infrastructure for training open source foundation models, with emphasis on autonomy and contributing in a dynamic team.

As a P&C Actuary, you will assess insurance risks, review regulatory compliance, support audit teams, and document findings while nurturing client relationships and contributing to practice development efforts.

Leads global AI quality, accreditation, and conformity assessment activities, including obtaining EU AI Notified Body designation, developing regulatory processes, and ensuring consistent certification practices. The role advises internal teams and customers, represents BSI in international technical forums, establishes competency criteria, supports regulatory programs, identifies process improvements, and shapes the AI and machine learning business strategy. It requires expertise in global AI regulations, quality management, legislation, technical product areas, stakeholder influence, and people leadership.

Lead and operationalize a company-wide AI governance program: build frameworks, assess AI risks and use cases, embed controls in SDLC, run governance council, liaise with Siemens consortium, handle regulatory compliance (EU AI Act, NIST AI RMF), produce reporting, and deliver training and documentation across Product, Engineering, Legal, Privacy, HR and Security.

The role involves designing, building, and deploying ML systems, supporting stakeholders, and operationalizing AI applications in Defence.

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Own the full business development and sales cycle across Europe, including prospecting, partnerships, stakeholder engagement, negotiation, contracting, and closing against revenue targets. Develop channel opportunities, support European go-to-market strategies, communicate customer ROI, attend industry events, and collaborate with Marketing, Product, technology partners, and agencies. Track funnel performance, improve acquisition processes, and share market insights while engaging apparel brands and retailers.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Lead cross-functional teams of machine learning engineers and data scientists, driving technical direction and fostering a collaborative environment to develop high-quality ML-driven capabilities.

Lead statistical strategy and delivery for Phase I-IV clinical trials embedded with a pharmaceutical sponsor. Design analyses, support protocol and reporting activities, oversee programming/CRO work, apply SAS/R (R Shiny)/Python and ML/DL methods for analyses and automation, and mentor junior statisticians.

Lead technical pre-sales for AI and data opportunities across the UK public sector. Shape solution strategies, conduct client workshops, lead bids and procurements, advise on responsible AI adoption, and develop differentiated AI propositions. Provide expertise in generative and agentic AI, machine learning, LLM solutions, data foundations, governance, evaluation, and production adoption. Build senior stakeholder relationships, support demonstrations and proofs of concept, mentor colleagues, and ensure proposed solutions are practical, secure, scalable, ethical, and deliverable.

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.