Top Machine Learning Jobs in Edinburgh

Own and grow the EMEA territory by building pipeline, managing full sales cycles, closing enterprise travel-tech deals, and developing partnerships with TMCs and travel providers. Represent the company at events, engage senior executives, and share market feedback with Product and Leadership to drive strategic outcomes.

The Principal Solutions Architect leads technical solution design, partners with hyperscalers, ensures architectural integrity, and drives customer engagement through compelling narratives and demos.

Lead platform product ownership bridging cloud and firmware strategy and execution. Manage and prioritize backlog, engage customers, collaborate with R&D, define releases, and drive agile delivery aligned with product leadership.

Own the Device Intelligence product roadmap for tier‑1 network service providers. Define PRDs, AI/LLM-assisted workflows, and technical requirements; lead discovery, backlog prioritization, and cross-functional delivery; engage strategic customers and support sales with competitive positioning.

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.

Lead design, development, and production deployment of scalable, secure Data & AI solutions on Databricks and Microsoft Foundry. Define AI technical roadmap, mentor an AI engineering squad, partner with stakeholders to prototype and scale use cases, and enforce AI governance, security, and ethical practices.

Lead design, development, and deployment of scalable Data & AI solutions on Databricks and Microsoft Foundry. Define AI technical roadmap, mentor an AI engineering squad, partner with stakeholders to prototype and scale AI products, and ensure governance, security, and ethical AI practices across enterprise platforms.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

The role involves building infrastructure for training open source foundation models, with emphasis on autonomy and contributing in a dynamic team.

As a P&C Actuary, you will assess insurance risks, review regulatory compliance, support audit teams, and document findings while nurturing client relationships and contributing to practice development efforts.

The role involves designing, building, and deploying ML systems, supporting stakeholders, and operationalizing AI applications in Defence.

Lead engineering systems and teams for an AI chat application, focusing on backend, mobile, and desktop. Oversee strategy, architecture, integration, and high-impact technical decisions for reliable user experiences.

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 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.

Lead the execution of production ML systems: build data pipelines, training workflows, evaluation and inference infrastructure, fine-tune large models (LoRA/QLoRA/SFT/DPO), optimize GPU deployments, and integrate ML into products while ensuring robustness, safety, and reliable user-facing performance.

Lead implementation of production-grade ML systems: data pipelines, training/evaluation workflows, scalable GPU inference, model fine-tuning (LoRA/QLoRA/SFT/DPO), deployment, and monitoring. Collaborate with product and application teams to ship reliable, efficient, and safe ML-powered features for real users while managing latency, cost, and robustness constraints.

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.

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.

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 own ML platform end-to-end: data/feature pipelines, training and inference infrastructure, model registry, deployment, monitoring, and agentic AI stack. Design integrations across teams and third parties, ensure reproducibility, reliability, cost control, governance and audit trails, and grow a small engineering function supporting data scientists.