Top Machine Learning Jobs in Edinburgh

Lead and scale Mimica's product organisation, define global product strategy and roadmap for AI-driven task/process mining and automation, mentor product and design teams, improve product operations and discovery, align cross-functional stakeholders, handle escalations, and hire and forecast team needs.

As an Engagement Manager, you will enhance customer utilization of BioCatch's Behavioral Biometric solutions, manage relationships, lead projects, and identify sales opportunities.

The Senior AI Transformation Consultant works with clients to identify AI opportunities, define robust business cases, and oversee AI project deployments, ensuring solutions deliver value.

One-year, full-time fellowship supporting design, delivery, and evaluation of interventions for smallholder farmers. Duties include research and analysis, pilot design, field implementation, M&E, and cross-team collaboration with heavy mentorship and rotating in-country placements.

The Product Marketing Director will drive global growth and reputation through market insights, effective messaging, product launches, and sales enablement in the cybersecurity space.

Lead the development and optimization of machine learning algorithms, owning the end-to-end model development and collaborating with Data Engineers for integration into live systems.

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

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.

The Applied Machine Learning Scientist will innovate and implement ML algorithms to enhance advertising performance, collaborate with engineers, and prototype in data-driven models.

Lead Analytics Engineer to architect and scale Operations data in BigQuery, build robust ETL and data models for reporting, ML and LLM use, drive re-architecture of domains, set DW best practices, own data governance and testing, lead through others, and provide on-call pipeline support.

Lead development of enterprise commercial intelligence: build data models (pipeline, forecasting, segmentation), deploy AI/ML for forecasting and churn, create dashboards, establish data governance, partner with senior leaders to drive insight-led decisions and adoption across global teams.

The Factory Software Engineer will implement technology solutions for data integration, collaborate with stakeholders, and ensure delivery of high-quality software solutions.

The Research Manager will lead AI-powered insight projects, manage client relationships, oversee team execution, and innovate using AI tools to improve insights delivery.

Deliver end-to-end data science projects: build and deploy ML/NLP and generative-AI solutions, design scalable data pipelines, integrate cloud services and APIs, implement RAG/vector-search applications, and collaborate with cross-functional teams to produce reusable tools and client-facing products.

The Senior Product Manager will lead AI product strategy at CoinMarketCap, focusing on user needs and data analysis to drive product development.

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.

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

The Forward Deployed Software Engineer will customize AI solutions for enterprise customers, maintain technical ownership, and bridge between support and engineering.

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.

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.

Lead development and deployment of AI systems, design scalable ML software, mentor junior engineers, and collaborate with teams to solve client challenges.

Transform operational data into actionable insights by building semantic models, reporting solutions and metrics (Power BI/Fabric, DAX, M, SQL). Ensure data quality, governance, access controls, troubleshooting, documentation, and stakeholder collaboration to enable self-service analytics.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.