Top Machine Learning Jobs in Edinburgh

Perform malware and threat research across the attack lifecycle, validate and improve Falcon detections, collaborate with engineering and ML teams, automate analysis, support external security testing, and translate findings into product protections.

Lead and grow a distributed engineering organization building customer-facing security capabilities: proprietary scanners, AI/ML detection engines, agentic remediation, vulnerability management, and security foundations. Set vision and roadmap, drive architecture and delivery, partner with product management, establish engineering standards, and represent the group in cross-functional and customer forums.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Provide clinical guidance across sales, implementation, product, and support teams for InterSystems health information solutions. Engage senior clinical and executive stakeholders, lead demonstrations and workshops, support procurement and digital transformation, and translate clinical requirements into solution narratives to drive adoption and product strategy.

Conduct postdoctoral research on avian influenza modeling in cattle using multi-scale simulation models, machine learning, and demographic, environmental, and phylogenetic datasets. Collaborate with UK and US universities, coordinate research activities, contribute to disease-control strategies, disseminate findings, and participate in international visits and project meetings focused on infectious disease research.

Provide technical leadership for production AI and machine learning systems, including generative AI, intelligent agents, predictive modeling, and optimization. Design scalable AI platforms, APIs, services, data pipelines, and infrastructure; establish evaluation, testing, observability, security, and governance standards; optimize system performance, reliability, and cost; and partner with multidisciplinary teams to deploy and operate solutions. Mentor engineers and influence technical strategy across the organization.

Provides technical leadership for production AI and machine learning systems, including generative AI, intelligent agents, predictive models, and optimization. Establishes architecture, engineering standards, evaluation and observability practices, and reusable AI platforms. Partners across business, engineering, data, risk, and control teams to deploy and operate scalable systems while optimizing performance, reliability, security, and cost. Mentors engineers and influences technical strategy.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Reviews and evaluates engineering, infrastructure, and platform documents and work products for technical accuracy, completeness, clarity, consistency, and relevance. Provides structured feedback, identifies errors and gaps, compares technical approaches, and follows defined evaluation frameworks. Requires independent work, strong communication, analytical ability, and professional engineering judgment. The role is fully remote and supports varied technical evaluation projects as an independent contractor.

Lead technical strategy and architecture for personalization, search, and recommendation systems. Deliver scalable ML-powered features, mentor senior engineers, and improve engineering quality across cross-functional teams.

As a Principal Engineer at M&S, you'll drive technical strategy, influence architecture, and mentor engineers while delivering on personalization, search, and machine learning features in collaboration with cross-functional teams.

Designs and delivers integrated customer data solutions supporting Thales UK’s digital and data strategy. Responsibilities include architecture design, data integration, proof-of-concept development, stakeholder engagement, technical documentation, data governance, solution oversight, and lifecycle management. The role supports UK Ministry of Defence data standards, collaborates with engineering and digital platform teams, evaluates emerging technologies, and develops secure data management and analytics solutions across complex defence environments.

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.

Owns roadmap execution and capability definition for an Agentic AI Platform. Translates AI platform strategy into requirements, prioritized roadmaps, reusable agent orchestration and lifecycle capabilities, governance controls, observability, and scalable platform patterns. Partners with engineering, architecture, data, security, legal, compliance, customer success, professional services, and customers. Measures adoption, reliability, safety, quality, cost, and business impact while balancing technical feasibility, security, compliance, and customer value.

Conduct mathematical and computational research on elastic and elasto-inertial turbulence in viscoelastic fluids. Develop and apply statistical and machine-learning methods to compare experiments with simulations, optimize simulation parameters, estimate polymer stresses, and investigate flow states across two- and three-dimensional regimes. Collaborate with researchers at the University of Edinburgh, University of Pennsylvania, University of Cambridge, and University of Vermont, with opportunities for international research travel.

Develop and deploy machine learning models that improve e-commerce search relevance, recommendations, product classification, and customer intent understanding. Responsibilities include collecting and preparing large datasets, feature engineering, predictive modeling, validation, testing, and leading work in areas such as NLP, computer vision, information retrieval, and deep learning. The role requires building scalable, reusable machine learning pipelines and collaborating across concurrent projects.

Leads the design and delivery of cybersecurity and fraud analytics solutions using Databricks, Python, SQL, R, AI/ML, LLMs, and computer vision. Directs data science, data engineering, and BI teams; manages concurrent engagements; investigates emerging threats; integrates new data sources; communicates findings to stakeholders; recommends remediation; and applies MLOps and model lifecycle practices in secure federal environments.

Build and extend the platform layer: design shared backend services and APIs, improve reliability and correctness, collaborate with product teams to remove platform bottlenecks, mentor engineers, shape architecture and migrations, and write high-quality fullstack code with a backend focus.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Own the end-to-end product direction and quality of AI-powered email capabilities. Define requirements, evaluation frameworks, quality standards, and measurable outcomes while balancing model quality, latency, cost, reliability, safety, and user experience. Partner closely with ML, backend, and client engineers on system design, experimentation, failure handling, and iteration. Drive prioritization, technical decisions, execution, and user trust in a fast-moving zero-to-one environment.

Own the end-to-end product strategy and execution for an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing reliability, latency, cost, safety, and usability. Partner closely with ML, backend, and client engineering teams to identify failure modes, improve system performance, prioritize work, and deliver predictable, trustworthy AI capabilities in a fast-moving zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing quality, latency, cost, reliability, safety, and usability. Partner closely with ML, backend, and client engineers to identify failure modes, prioritize improvements, run experiments, and deliver predictable, trustworthy AI features in a fast-moving zero-to-one environment.

Owns the end-to-end definition, evaluation, and quality of AI-powered email capabilities. Partners with ML, backend, client, and product teams to translate model behavior and technical constraints into requirements, system decisions, experiments, and roadmaps. Balances quality, latency, cost, safety, reliability, and user experience while identifying failure modes and building trustworthy, predictable AI features.