Top Machine Learning Jobs in Edinburgh

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Own the full business development and sales cycle across Europe, including prospecting, partnerships, stakeholder engagement, negotiation, contracting, and closing against revenue targets. Develop channel opportunities, support European go-to-market strategies, communicate customer ROI, attend industry events, and collaborate with Marketing, Product, technology partners, and agencies. Track funnel performance, improve acquisition processes, and share market insights while engaging apparel brands and retailers.

Lead technical pre-sales for AI and data opportunities across the UK public sector. Shape solution strategies, conduct client workshops, lead bids and procurements, advise on responsible AI adoption, and develop differentiated AI propositions. Provide expertise in generative and agentic AI, machine learning, LLM solutions, data foundations, governance, evaluation, and production adoption. Build senior stakeholder relationships, support demonstrations and proofs of concept, mentor colleagues, and ensure proposed solutions are practical, secure, scalable, ethical, and deliverable.

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

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

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.

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 a Senior Impact Analyst, you will analyze sustainability challenges, design and manage interventions, and work closely with smallholder communities, emphasizing rigorous research and collaboration.

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.

Lead enterprise sales for Everpure's AI/HPC data infrastructure, driving large deals with F1000 companies. Build executive relationships, lead cross-functional pursuit teams, influence product roadmap, manage a pipeline to exceed revenue targets, and evangelize all-flash storage and data pipeline solutions for AI workloads.

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 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.

Identify, qualify, and nurture enterprise leads for Cohere's AI solutions through outbound outreach, BANT qualification, Salesforce pipeline management, and collaboration with marketing. Run smaller sales cycles to close opportunities and stay current on industry and product trends.

As a P&C Actuary, you will assess risks for insurers, support audit teams, and maintain compliance with regulations, while growing client relationships and improving tools for efficiency.

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

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.

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 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.

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.

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 infrastructure for training, evaluation, deployment, inference, and experimentation. Improve reliability, scalability, latency, throughput, and cost efficiency. Create reproducible pipelines, observability, benchmarking, and platform tooling enabling researchers and engineers to ship models to production.

As a Lead Machine Learning Engineer, you will set technical direction for ML projects, design scalable systems, mentor engineers, and drive innovation. You'll ensure successful project outcomes while balancing trade-offs and guiding team priorities.

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