Top Machine Learning Jobs in Edinburgh

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.

Lead ML strategy and delivery for Workforce Management: forecasting, routing, assignment, escalation and time-to-completion prediction. Build, ship and maintain production ML systems, coach and grow a team of ML scientists, work cross-functionally to turn operational problems into pragmatic ML solutions, and improve customer and operational outcomes.

Lead end-to-end development and productionisation of time-series forecasting ML solutions: design, train, deploy models, build cloud-native data pipelines, collaborate with stakeholders, and monitor model performance to optimise clinic scheduling and staffing.

The Machine Learning Engineer will build, refine, and optimize AI/ML prototypes while collaborating with teams on technical documentation, deployment, and model integration.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Lead end-to-end ML initiatives: integrate and fine-tune models, optimize GPU inference for latency and throughput, build evaluation and monitoring tooling, collaborate on scalable serving systems, and mentor engineers.

The Senior ML Data Engineer will enhance data workflows, collaborate with teams to build datasets, influence data strategies, and implement pipelines for effective data usage in AI/ML products.

The Staff Machine Learning Engineer will lead the execution of ML systems, ensuring reliability and scalability while collaborating with cross-functional teams to deliver high-quality AI solutions.

The role involves building end-to-end production-grade ML systems, optimizing pipelines, integrating models into applications, and maintaining data systems.

As Technical Lead, you'll own ML system execution, manage data pipelines, model deployment, and ensure performance under real-world conditions while collaborating with the team.

The VP of Research, Machine Learning will lead the research direction for AI systems, focusing on intelligence design, evaluation frameworks, and advanced machine learning techniques while ensuring real-world applicability.

The Senior Machine Learning Engineer will build core ML systems, handle data preparation and iteration, debug model issues, and ensure reliable production performance while mentoring other engineers.

Design, build, and maintain production-grade ML systems for revenue management. Own end-to-end ML lifecycle: data pipelines, model deployment, monitoring, testing, and scaling. Collaborate with product and engineering to define SLIs/SLOs, optimize pricing strategies, and mentor cross-functional teams to ensure reliable, high-quality ML-driven features.

Full-stack AI/ML Software Engineer to build HealthTech SaaS microservices, implement and integrate ML solutions, write high-quality code, author documentation, perform code reviews, and act as an SME on services and systems.

Build, train, evaluate and productionise custom ML and LLM models for customer feedback: fine-tuning pipelines, retrieval/reranking, summarisation, sentiment, evaluation frameworks, and monitoring. Collaborate with engineering, product and commercial teams, mentor colleagues, run experiments, and translate business needs into practical ML solutions.

Lead finance analytics, own data warehouse strategy and governance, build a reporting Center of Excellence, implement Finance AI automation and data pipelines, optimize GCP costs, create self-service analytics, manage a small cross-functional team, and ensure data governance and audit readiness.

Lead pricing ML for a real-time energy transaction engine: define mathematical formulation, build and ship production-grade models, optimise pricing and portfolio balance, and communicate model behavior to commercial and engineering stakeholders.

Lead pricing ML for Rosso: define technical direction, build production-grade price-optimisation and probabilistic imbalance models, bridge modelling and MLOps, and communicate model decisions across teams.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead and grow a team of ML scientists to build and productionise credit underwriting, pricing, limits, and lifecycle models. Define Borrowing's ML roadmap for UK growth, new segments, and EU expansion, improve feature engineering and data use, and raise standards for model quality, governance, and iteration speed.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

As a Staff/Principal Machine Learning Engineer, you will optimize and scale multimodal inference systems, ensuring high performance and reliability in production environments.

Lead development and productionisation of custom ML and LLM systems for customer feedback: fine-tune and evaluate models, build retrieval/reranking and agentic features, design evaluation frameworks, collaborate with Engineering and Product, mentor team members, and drive model-serving and monitoring work.

Design, scale, and productionize ML and physics-based scientific code for drug and materials discovery. Transition prototypes to robust products, orchestrate distributed GPU training, implement hardware optimizations, and support model lifecycle and integration into large-scale simulation frameworks.