Top Machine Learning Jobs in Edinburgh

Lead a financial crime data science team focused on detecting, preventing, and mitigating financial crime. Partner with Product, Risk, Engineering, and control owners to shape data strategy, generate strategic insights, improve product decisions, and deliver rigorous analytics and machine learning solutions. Hire, develop, and retain data scientists and analysts while supporting innovation through business intelligence, experimentation, causal inference, and self-serve analytics.

Design and develop cloud-based data platform solutions, including data pipelines, ingestion, transformation, cleansing, integrations, and data applications. Translate technical requirements into detailed designs, write reliable code, develop test automation, troubleshoot operational issues, tune batch and real-time processing, secure cloud data platforms, and implement CI/CD and monitoring. The role also involves advising client developers and defining efficient operational processes.

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 end-to-end machine learning systems across data, training, fine-tuning, evaluation, inference, deployment, and monitoring. Build scalable GPU-based infrastructure and reliable production pipelines for large models, optimize performance and cost, and partner with research and application engineering to translate model capabilities into product improvements. Provide hands-on technical leadership, resolve production issues, and ensure measurable, safe, and maintainable ML iterations.

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

Lead technically complex product work on Rosso, embedding with ML, data science and engineering teams to turn business and customer needs into shipped features. Define success criteria for models/forecasts, manage edge cases and operational risk, and prioritize work across portfolio, forecasting, pricing optimisation and related systems.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production infrastructure. Build smart payment-routing models and MLOps practices, partner with product, data, engineering, and customers, evaluate outcomes statistically, mentor engineers, and help define ML technical direction and reliable development processes.

Own an AI stockbroking product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, security, scalability, and failure handling. Integrate KYC, transaction monitoring, privacy, licensing, and regulatory requirements. Apply AI with evaluation, safeguards, and human oversight while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Own an AI investing product vertical end-to-end, including strategy, roadmap, technical design, delivery, metrics, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Embed regulatory requirements across KYC, monitoring, limits, privacy, and licensing. Apply AI with evaluation, safeguards, human oversight, and reliability controls while aligning compliance, operations, finance, treasury, and design.

Owns an AI neobank product vertical end-to-end, including strategy, roadmap, technical design, delivery, regulatory requirements, AI evaluation, reliability, and launch. Partners with engineering, compliance, operations, treasury, finance, and design across payments, remittance, insurance, wealth, and trading products. Must balance correctness, scalability, security, user experience, cost, speed, and regulatory constraints in high-consequence financial systems.

Lead application engineering for an AI email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, guide a senior engineering team, make architecture and reliability trade-offs, and establish observability, monitoring, testing, and operational excellence. Partner with ML leadership to ship scalable, maintainable, and high-performing AI-powered experiences.

Lead the engineering strategy and execution for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, lead a senior engineering team, make architecture and scalability decisions, and establish strong observability, monitoring, testing, security, and operational foundations. Partner with ML leadership to deliver fast, reliable, maintainable product experiences.

Own the end-to-end product direction and quality of AI-powered systems. Define requirements, evaluation frameworks, quality bars, and feedback loops while balancing model quality, latency, cost, reliability, safety, and user experience. Partner deeply with ML, backend, and client engineers on system design and iteration, identify failure modes, and drive execution in a fast-moving zero-to-one environment.

Leads application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Designs reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Builds APIs connecting AI capabilities to product experiences, makes architectural trade-offs, establishes observability and operational excellence, and leads a small senior engineering team through mentorship, design reviews, and code reviews. Partners with ML leadership to deliver scalable, reliable, and maintainable production systems.

Lead application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI systems to product experiences, make architecture and scalability decisions, mentor senior engineers, and establish strong observability, monitoring, testing, security, and operational practices. Partner with ML leadership to ship fast, reliable, maintainable product experiences.

Lead developer relations for NVIDIA technologies across the EMEA energy and utilities ecosystem. Build strategic relationships with power, utility, grid, simulation, digital twin, and renewable energy software providers. Guide partner onboarding, integration, technical enablement, and co-innovation while identifying growth opportunities and shaping adoption strategies. Collaborate with engineering, product, marketing, and solution architecture teams; communicate technical concepts to diverse audiences; and advocate for partner feedback to influence product roadmaps.

Develop and validate agentic AI capabilities for patent analysis using machine learning, NLP, LLMs, RAG, hybrid search, and agentic workflows. Analyze large-scale intellectual property datasets, design experiments, evaluate and benchmark models, define reasoning strategies, and translate findings into product recommendations. Collaborate with product managers, IP experts, and engineers to deliver scalable, customer-facing solutions.

Develop and validate agentic AI capabilities for patent analysis using machine learning, NLP, LLMs, generative AI, retrieval, and hybrid search. Define multi-step reasoning workflows, analyze large-scale intellectual property datasets, establish model evaluation and benchmarking practices, and translate experiments into product recommendations. Collaborate with product teams, intellectual property experts, and engineers to deliver scalable, customer-facing solutions.

Leads complex machine learning delivery and Defence-sector strategy, translating client challenges into technical roadmaps, scoping projects, managing client relationships, and supporting technical sales. Writes production-grade Python, serves as a machine learning authority, develops Generative AI and agentic AI solutions using LLMs, and mentors and manages data scientists.

Lead and scale engineering teams building multi-tenant Voice AI, Agent QA, analytics, auto-learning, and workforce management platforms. Own architecture for high-throughput voice data systems, real-time pipelines, telephony integrations, optimization algorithms, and enterprise compliance. Recruit and develop senior engineers and managers, establish engineering standards, guide cross-functional roadmap alignment, and deliver reliable production systems across global geographies.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

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 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.