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.

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.

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.

Lead data visualization and AI interaction initiatives within Accenture’s Augmented Insights practice. Apply data analysis, modeling, storytelling, user-centered design, SQL, data quality, data warehousing, big data, AI, and machine learning expertise to help clients solve business problems. Responsibilities include stakeholder engagement, consulting, requirements gathering, business analysis, and leading multidisciplinary teams across client projects.

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.

Provide expert technical advice on age assurance and digital identity technologies within online safety regulation. Lead research and evaluations, assess technical feasibility and risks, translate complex concepts for policy and leadership audiences, prepare briefings and demonstrations, monitor emerging trends, and engage with academia, industry, public bodies, and external technical groups.

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.

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.

Supports enterprise sales by discovering customer requirements, mapping them to SailPoint solutions, and delivering foundational implementations and proof-of-value engagements. Acts as a technical lead and consultative advisor, educating customers on architecture and best practices. Collaborates with sales, product, professional services, partners, and customer success teams while developing AI-driven and agentic solutions for demonstrations and customer outcomes.

Leads the end-to-end design of data solutions, including ingestion, transformation, data modeling, and cloud data platform architecture. Defines target-state solutions using Databricks, Azure, and AWS across data warehouses, lakes, and lakehouses. Designs data product architectures, ownership frameworks, and solutions supporting AI and analytics. Collaborates with architects, engineers, platform owners, and analysts while documenting and presenting complex solutions to senior and non-technical stakeholders in regulated environments.

Lead and mentor a data science team developing machine learning systems for threat detection and agentic AI. Set ML technical strategy, standards, roadmaps, and deployment practices; guide projects from research through production; collaborate with security, engineering, and product teams; develop advanced malware and behavioral threat models; communicate insights; and contribute to research, publications, patents, and hands-on technical work.

The role involves designing scalable data pipelines, implementing custom ML algorithms, and working on microservice architectures for ad optimization models.

Lead real-world evidence biostatistical analyses using large-scale healthcare data to support clinical development, regulatory strategy, HEOR, and payer decisions. Design observational studies and target trial emulations, apply causal inference, statistical learning, machine learning, and NLP, and build reproducible Python/R workflows. Collaborate with clinical, epidemiology, data engineering, and HEOR teams while translating findings for technical and non-technical stakeholders. Support regulatory submissions, governance, audits, and scientific dissemination.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

Leads the cybersecurity operations team and oversees 24/7 monitoring, threat detection, incident response, vulnerability management, security controls, and third-party security providers. Directs major incident containment, forensic investigations, post-incident reviews, and risk-based remediation. Manages staff, vendors, KPIs, and operational resources while briefing leadership on security posture and emerging threats. Also establishes controls for AI and ML workloads and operationalizes institutional security policies.

Leads the design, development, deployment, and governance of scalable Data and AI solutions using Databricks, Microsoft Foundry, and Azure. Defines AI technology roadmaps, mentors engineering teams, identifies high-value business use cases, prototypes and scales AI products, and ensures secure, ethical, enterprise-integrated implementations.

As a Principal Engineer at M&S, you'll drive technical strategy, influence architecture, and mentor engineers while delivering on personalization, search, and machine learning features in collaboration with cross-functional teams.

Analyze complex datasets, develop dashboards and reports, translate stakeholder requirements into data-driven solutions, and support analytical models and AI-enabled capabilities. The role involves collaborating with engineers and multidisciplinary Agile teams, ensuring data quality and documentation, presenting insights, and supporting UX/UI design within a secure Defence programme.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Deliver enterprise and operational risk transformation and advisory services for financial institutions. Assess and enhance risk processes, controls, governance, market abuse frameworks, and trade surveillance capabilities. Support AI-enabled analysis, surveillance model tuning, workflow automation, validation, auditability, and responsible AI practices. Collaborate with business, technology, compliance, and risk stakeholders to identify control gaps and implement data-driven improvements.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Build and deploy end-to-end data science and AI solutions, including scalable data pipelines, machine learning and generative AI workflows. Translate product and business questions into measurable analytics use cases, analyze product data, establish evaluation and monitoring practices, and collaborate with product, engineering, and analytics teams to deliver business impact.

Design and lead architecture for agentic AI, knowledge graph, semantic, and enterprise data platforms supporting supply chain systems. Build reusable data models, ingestion pipelines, integrations, entity resolution, schema alignment, validation, and graph capabilities. Evaluate emerging technologies, define scalable architecture and governance patterns, establish quality controls and evaluators for agentic systems, and mentor technical teams while influencing product and engineering direction.

Lead the transformation of legacy products into a unified enterprise platform. Translate strategy into roadmaps, manage backlogs, prioritize cross-functional initiatives, and coordinate Engineering, UX, AI, Data Science, Commercial, and Operations teams. Drive platform discovery, delivery, launch, adoption, and measurable business outcomes. Integrate trustworthy AI and data capabilities into customer-facing workflows while influencing senior stakeholders and balancing customer value, commercial goals, technical feasibility, risk, and resources.