Top Machine Learning Jobs in Edinburgh

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.

CrowdStrike seeks a Senior Intelligence Analyst to provide threat intelligence and digital risk monitoring, collaborating with teams to deliver customer-focused solutions and presentations, while maintaining customer relationships.

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.

As a Product Manager at Bringg, you will define product strategy, work with stakeholders, manage the roadmap, and integrate AI into logistics solutions.

Lead design and delivery of secure, scalable enterprise software for private banking. Build reusable frameworks, integrate AI/ML, ensure operational stability, mentor engineering teams, and influence senior stakeholders to drive digital transformation.

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.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

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.

Design, develop, and maintain the core backend of a cloud-based distributed video management system. Build new features, increase capacity, manage CI/CD and customer deployments, and ensure code quality, security, and scalability while collaborating within an agile team.

The VP of AI Platform Engineering will lead AI development at BlackRock, focusing on scalable architecture, team management, and cross-functional coordination to enhance engineering capabilities and empower technologists.

Design and implement large-scale AI/ML systems using LLMs and RAG. Develop data pipelines, optimize models, and collaborate with teams on actionable intelligence solutions.

Lead a team of data scientists to build and maintain data pipelines, clean and transform data, develop statistical and machine learning models, deliver predictive analytics, and partner with stakeholders to drive business value and governance.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Lead market pricing for the UK pet insurance book: develop demand, elasticity, and price-optimisation models, monitor competitors and market trends, make rate recommendations, present findings to stakeholders, support pricing strategy, and line-manage one pricing analyst.

Build and validate verification systems to identify high-quality expert participants. Define problems, generate hypotheses, run experiments, prototype ML/statistical solutions, and turn approaches into production-ready models while collaborating across teams.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

Lead identification, development, and global deployment of AI and automation solutions to optimize forms and precedents content operations. Prioritize opportunities, collaborate with cross-functional teams, ensure governance and quality, integrate solutions, report outcomes to leadership, and drive continuous improvement and responsible AI practices.

The AI Research Engineer will develop AI models to enhance product autonomy, analyze extensive data, and deliver impactful solutions, driving innovative product features.

As a Lead Data Scientist, you will set technical direction, ensure project delivery, manage client relationships, and mentor data scientists.

The Computer Vision Engineer will lead AI initiatives in the defense sector, delivering solutions and mentoring teams while driving technical strategy and capability development.

Create clear, customer-focused documentation and help content for cloud-based video security, access control, and intrusion platforms. Design user-centric guides, peer-review content for quality and consistency, and collaborate with Engineering, Support, Escalations, and UX teams to improve documentation and product usability.

Lead development of a modern first-line technology control environment, embedding automated, preventative and real-time controls across cloud-native platforms. Partner with Technology, Data and AI teams to support resilience, cloud migration and platform transformation. Use data-driven monitoring to improve control effectiveness, influence senior leaders, and build a high-performing Control Office team.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead and develop a team of ML specialists to deliver production ML systems for fraud and financial crime. Set priorities, ensure model lifecycle practices (development, deployment, monitoring, governance), partner with Product, Risk, Engineering and Data, and raise standards for performance, explainability, reproducibility and observability while building an inclusive high-performing team.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.