Top Machine Learning Jobs in Edinburgh

Design and build simulation environments for robotics research, implement RL algorithms, and evaluate policies for stability and transfer potential. Collaborate with teams and document results.

The Presales Engineer will address financial crime compliance needs by engaging clients, presenting solutions, leading POCs, and enhancing customer relationships.

As an Engagement Manager, you will enhance customer utilization of BioCatch's Behavioral Biometric solutions, manage relationships, lead projects, and identify sales opportunities.

As a Sr. Account Executive, you will sell and develop customer relationships while building your territory, focusing on customs clearance efficiencies.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

As an Account Executive, you'll drive new business growth, manage sales opportunities, understand client needs, and ensure customer satisfaction within the SaaS sector, focusing on enterprise solutions.

The role involves designing scalable data pipelines, implementing custom ML algorithms, and working on microservice architectures for ad optimization models.

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.

The Senior AI Transformation Consultant works with clients to identify AI opportunities, define robust business cases, and oversee AI project deployments, ensuring solutions deliver value.

Manage the end-to-end product lifecycle for financial crime tools, focusing on compliance, innovation, and backlog management while liaising between engineering and business teams.

Lead the LLM team to architect, train, and deploy AI models, optimizing real-time user experiences and creating advanced moderation systems.

The Applied Machine Learning Scientist will innovate and implement ML algorithms to enhance advertising performance, collaborate with engineers, and prototype in data-driven models.

The AI/ML Engineer will develop and implement ML systems for secure-code understanding, manage infrastructure for LLMs, optimize models, and conduct research.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.

Own and drive ML solutions for clinical care at scale, including LLM fine-tuning and production deployment, collaborating across teams.

The Factory Software Engineer will implement technology solutions for data integration, collaborate with stakeholders, and ensure delivery of high-quality software solutions.

The Research Manager will lead AI-powered insight projects, manage client relationships, oversee team execution, and innovate using AI tools to improve insights delivery.

The Senior AI/ML Developer will deliver AI features, integrate ML models, and oversee the full project lifecycle from concept through production deployment.

Build MCP servers and agentic workflows that securely connect LLMs to enterprise systems (Salesforce, Snowflake, Google Workspace), deliver production AI integrations, collaborate with stakeholders, document patterns, and grow the AI Enablement function.

As a Lead Machine Learning Engineer, you'll set technical directions for AI projects, design scalable ML systems, lead project delivery, and mentor a team of engineers while collaborating with clients to achieve their goals.

The Head of Machine Learning will manage 9 ML engineers, coordinate projects, contribute to system architecture, and promote team growth. Responsibilities include strategic leadership, project management, and technical mentorship to align ML initiatives with business goals.

As a Machine Learning Engineer, you will design, build, and deploy ML systems, manage AI project methodologies, and support delivery with technical expertise, collaborating with cross-functional teams and stakeholders.

Lead the development and deployment of AI systems, designing scalable ML software and infrastructure while mentoring junior engineers and advising clients.

The role involves training and evaluating AI models, conducting AI training tasks, and providing expert feedback on their performance using machine learning skills.

AI Trainers will complete tasks involving analyzing, editing, and annotating data to improve AI models with real ML expertise, including performance evaluation and providing technical feedback.