Top Machine Learning Jobs in Edinburgh

Manage and expand strategic partner alliances for SailPoint’s enterprise software business. Develop joint business, marketing, technical certification, and go-to-market plans; generate partner-sourced pipeline; coordinate account mapping, co-selling, enablement, promotions, and implementation capacity. Serve as the primary liaison between SailPoint and partner organizations, monitor performance and KPIs, ensure compliance and delivery quality, and present quarterly business assessments. The role requires extensive enterprise partner-management experience, analytical skills, executive communication, and approximately 50% travel.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Manage a team of machine learning engineers developing credit and lending models for international markets. Set technical strategy, guide advanced ML solutions, partner with product, engineering, analytics, design, and risk teams, and ensure scalable underwriting capabilities. Provide hands-on technical leadership, clarify complex problems, manage trade-offs, and mentor engineers while contributing to Affirm’s long-term machine learning strategy.

Provides independent, risk-based security assurance for AI capabilities across cloud, SaaS, hybrid, and internally developed environments. Reviews AI architectures, controls, risks, monitoring, and security posture; validates findings and remediation; develops AI assurance frameworks, inventories, criteria, and control baselines; and advises stakeholders on pragmatic risk treatment. Key focus areas include prompt injection, data exposure, model poisoning, identity, guardrails, supply-chain security, resilience, and incident response.

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.

Lead research developing interpretable AI-for-science methods for quantum computing. Responsibilities include designing research software and experiments, evaluating AI-assisted scientific work, conducting failure analysis, collaborating with quantum researchers, publishing findings, releasing research artefacts, coordinating technical work, supporting junior researchers, and contributing to future proposals.

Designs and optimizes scalable data pipelines and data solutions supporting actuarial modeling and assumption setting. Ensures data quality, governance, validation, monitoring, and audit controls across complex financial-services datasets. Translates business requirements into technical solutions, integrates data sources, applies machine learning and advanced analytics, and improves platform performance. Provides technical guidance, reviews code and analysis, coaches junior colleagues, and contributes to data engineering standards and continuous process improvement.

Leads Citi’s global Complex Assets franchise across OTC derivatives post-trade services, middle office, and fund accounting. Owns enterprise strategy, P&L, operations, risk controls, client relationships, organizational leadership, technology transformation, and cross-business collaboration. Develops transformation roadmaps, drives innovation using AI, machine learning, cloud infrastructure, and automation, and represents the business with executive stakeholders, institutional clients, industry bodies, and regulators.

Conduct original research in embodied and spatial AI, focusing on 3D perception, spatial reasoning, multimodal models, world models, navigation, learning from interaction, and intelligent agents. Publish research, collaborate across AI and robotics, help shape the research programme, and support supervision and mentoring of student projects.

Design and develop cloud-based data platform solutions, including data pipelines, ingestion, transformation, cleansing, integrations, and data applications. Translate technical requirements into detailed designs, write reliable code, develop test automation, troubleshoot operational issues, tune batch and real-time processing, secure cloud data platforms, and implement CI/CD and monitoring. The role also involves advising client developers and defining efficient operational processes.

Serve as a technical advisor to ISPs throughout discovery, demonstrations, proof-of-concepts, integration, deployment, and support. Integrate Plume solutions with customer BSS/OSS, CRM, ticketing, and AI platforms. Partner with sales to drive revenue, deliver presentations and QBRs, support trials, provide product feedback, and stay current on networking, cloud, and AI technologies. The role is remote within the UK or Netherlands and requires travel exceeding 70%.

Lead AI engineering innovation across conversational analytics and emerging analytical AI products. Develop governed, testable, scalable solutions using Python, SQL, APIs, cloud technologies, and automated workflows. Prototype and evaluate analytical agents, anomaly detection, process intelligence, and intelligent workflows. Establish engineering standards, evaluation frameworks, testing approaches, documentation, and reusable assets while partnering with product, data, governance, security, privacy, risk, and architecture teams to move successful concepts into production.

Lead and build a new platform engineering team responsible for cloud-agnostic deployments across AWS, other clouds, private cloud, and on-premises environments. Own Kubernetes infrastructure, Terraform, GitOps, CI/CD, observability, reliability, compliance automation, FinOps, and AI/ML infrastructure. Hire and develop engineers, establish operating standards, support SOC 2, ISO 27001, and GDPR controls, and deliver portable, secure, auditable platform foundations for data catalog, knowledge graph, and integration services.

Own and build Oversee’s product marketing function, including ICP definition, positioning, persona messaging, competitive and win/loss research, product content, sales enablement, and end-to-end product launches. Partner closely with product, sales, growth marketing, and executives to translate technical AI and travel technology capabilities into compelling commercial messaging, establish repeatable GTM processes, and measure launch performance.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Own and develop production machine learning components for a dynamic pricing system. Build probabilistic models, experimentation and measurement systems, monitoring, alerting, guardrails, staged releases, and replayable pricing decisions. Investigate commercial pricing adjustments, improve business metrics, and collaborate with engineering, product, platform, sales, and commercial teams. The role requires hands-on production coding, operational accountability, and safe deployment of automated decisions involving money.

Build and scale the ML platform supporting production forecasting, pricing, prediction, and optimization models. Responsibilities include deployment infrastructure, model inference and training, observability, monitoring for drift and degradation, data validation, evaluation and backtesting tooling, production data systems, and reliability ownership. The role also contributes to model improvements, time-series forecasting, and potentially probabilistic modeling and quantitative trading or energy optimization applications.

Own product and research launch marketing from technical positioning through distribution. Translate AI research, training runs, and benchmarks into blogs, explainers, social content, landing pages, documentation, sales collateral, and campaigns. Engage technical communities, coordinate cross-platform launches, and use analytics to optimize reach, engagement, traffic, and adoption. The role requires strong technical writing, product marketing judgment, rapid execution, and experience communicating AI, ML, or developer tools to developers and enterprise audiences.

Lead the design and delivery of modern data and AI solutions for multiple customers. Provide architectural direction, technical assurance, and thought leadership while advising senior stakeholders on strategic and technical decisions. Manage and mentor architects and engineers, develop standards and account strategies, support agile teams, build customer relationships, and guide scalable, distributed, cloud-based applications. The role also involves business development, technology advocacy, and fostering inclusive, high-performing teams.

Own the strategy, roadmap, development lifecycle, and go-to-market activities for fraud analytics products. Lead customer discovery, prioritize product improvements, analyze complex data, collaborate with development and data science teams, and use market and customer insights to improve user outcomes. The role requires fraud prevention expertise, statistical modeling and AI/machine learning knowledge, strong stakeholder communication, and flexible UK-US working hours.

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.

Conduct academic research under the King-Smith Group to develop machine learning models for identifying novel therapeutic candidates. The role involves applying machine learning methods to chemical sciences, completing research projects, and communicating findings effectively. The position is a full-time, 18-month fixed-term postdoctoral appointment, with part-time and flexible working options considered.

Lead strategic relationships with Capital Markets organizations across EMEA, driving adoption of NVIDIA AI and accelerated computing solutions. Serve as the primary client contact, grow revenue and platform usage, develop account strategies, forecast business, and engage senior technology leaders. Collaborate with solution architects, business development, developer relations, cloud partners, hardware providers, and software developers. Communicate solution value, provide thought leadership, and travel to meet clients and partners.

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.

Lead technical discovery, solution design, demonstrations, and proof-of-concept support for Anaconda’s AI and data science platform across EMEA. Partner with sales on complex opportunities, translate technical capabilities into business value, create reusable technical content, represent customer feedback to product teams, and coordinate cross-functional handoffs through implementation. Manage multiple concurrent sales cycles while supporting pipeline growth, deal expansion, and faster closes.