Top Machine Learning Jobs in Edinburgh

Develop and implement deep-learning and computer-vision models for histology imaging to create spatial biology biomarkers. Collaborate with engineers, clinicians and product teams, build scalable data pipelines, manage regulatory/product inputs to translate research into clinical-grade tools, and communicate results via stakeholders and publications.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

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 Senior Applied ML Engineer will build and maintain production ML systems for catalogue decision-making, improving data quality, product categorization, and discoverability.

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

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

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.

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

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.

Design, train, evaluate, and deploy predictive transportation and delivery ML models (primarily XGBoost). Perform EDA, feature engineering, experiment design, and statistical evaluation. Build production ML pipelines and MLOps practices on Google Vertex AI. Collaborate with data engineering and supply chain stakeholders to translate operational needs into scalable solutions.

Lead and own the ML function end-to-end for Rosso: set ML strategy, hire and develop ML engineers, build operating systems (cadences, monitoring, incident review), drive ML-software collaboration, and be accountable for ML outcomes across forecasting, pricing, optimisation, and classical ML.

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.

Lead and own tem's ML function (Rosso): set ML strategy, hire and develop ML engineers, establish operating systems (monitoring, incident reviews, prioritisation), and drive close collaboration with software engineering to deliver forecasting, pricing, and optimisation models that meet business outcomes.

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.

Build and operate ML platform systems that enable teams to train, evaluate, deploy and serve models. Work on backend services, Python libraries, model lifecycle tooling, evaluation workflows and low‑latency serving using Go/Python, Kubernetes and cloud infrastructure, prioritizing reliability, observability, safety and developer experience.

As a Staff ML Research Scientist, you will develop advanced ML models for drug discovery, integrating them into products and mentoring junior researchers.