Top Machine Learning Jobs in Edinburgh

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.

Lead and grow a distributed engineering organization building customer-facing security capabilities: proprietary scanners, AI/ML detection engines, agentic remediation, vulnerability management, and security foundations. Set vision and roadmap, drive architecture and delivery, partner with product management, establish engineering standards, and represent the group in cross-functional and customer forums.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Provide clinical guidance across sales, implementation, product, and support teams for InterSystems health information solutions. Engage senior clinical and executive stakeholders, lead demonstrations and workshops, support procurement and digital transformation, and translate clinical requirements into solution narratives to drive adoption and product strategy.

Lead and manage an ML engineering team building post-origination credit models for repayment, collections, recovery, and loss mitigation. Define technical strategy, design and scale ML solutions, partner with product, engineering and risk, mentor engineers, and operationalize models to minimize loan loss and personalize collections.

Lead applied AI research and architecture for GenAI in cybersecurity: design LLM architectures, implement production-grade deep learning systems at scale, mentor scientists, set technical standards, and align AI strategy with business goals.

Provide technical leadership for production AI and machine learning systems, including generative AI, intelligent agents, predictive modeling, and optimization. Design scalable AI platforms, APIs, services, data pipelines, and infrastructure; establish evaluation, testing, observability, security, and governance standards; optimize system performance, reliability, and cost; and partner with multidisciplinary teams to deploy and operate solutions. Mentor engineers and influence technical strategy across the organization.

Provides technical leadership for production AI and machine learning systems, including generative AI, intelligent agents, predictive models, and optimization. Establishes architecture, engineering standards, evaluation and observability practices, and reusable AI platforms. Partners across business, engineering, data, risk, and control teams to deploy and operate scalable systems while optimizing performance, reliability, security, and cost. Mentors engineers and influences technical strategy.

Conduct mathematical and computational research on elastic and elasto-inertial turbulence in viscoelastic fluids. Develop and apply statistical and machine-learning methods to compare experiments with simulations, optimize simulation parameters, estimate polymer stresses, and investigate flow states across two- and three-dimensional regimes. Collaborate with researchers at the University of Edinburgh, University of Pennsylvania, University of Cambridge, and University of Vermont, with opportunities for international research travel.

Manage relationships with AI-native startups, scaleups, and labs across the UK. Understand customer goals, develop AI solution strategies, drive adoption and growth, coordinate technical and business resources, build customer references and case studies, identify emerging use cases, and represent NVIDIA at industry events. The role requires deep AI ecosystem knowledge, strategic planning, cross-functional communication, and understanding of NVIDIA’s software, server, and cloud stack.

Develops NVIDIA’s Financial Services business in the UK by managing strategic accounts, building customer and partner ecosystems, identifying market opportunities, and promoting technology-enabled solutions. The role creates business plans, cultivates lighthouse customers and AI software partners, develops use cases and case studies, supports community marketing, and represents NVIDIA at industry events. It requires strong account management, partnership development, communication, strategic analysis, and knowledge of financial services applications, software, servers, and cloud services.

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.

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.

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.

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.

Lead engineering systems and teams for an AI chat application, focusing on backend, mobile, and desktop. Oversee strategy, architecture, integration, and high-impact technical decisions for reliable user experiences.