Top AI Jobs in Edinburgh

The Managing Consultant will deliver Human Factors projects, ensuring effective collaboration, technical leadership, and management of safety improvement programs, while mentoring juniors and supporting business development.

Design and implement large-scale AI/ML systems using LLMs and RAG. Develop data pipelines, optimize models, and collaborate with teams on actionable intelligence solutions.

Join a pioneering team in post-quantum cryptography, working on RISC-V based security solutions. Collaborate on innovative technologies in a flexible environment.

The role involves designing and implementing machine learning solutions, managing projects from idea to deployment, and optimizing models using AWS and Docker.

The Director of AI will drive AI strategy across the company, manage R&D efforts, lead adoption of AI workflows, and establish success metrics.

The role involves deploying AI solutions at client sites, requiring technical expertise alongside strong problem-solving skills in a collaborative environment.

Define the strategic vision for the Video-Based Safety platform, integrating AI and Computer Vision to enhance safety and efficiency in physical operations. Lead cross-functional teams and mentor product managers while driving innovation in a rapidly growing company.

As Senior Product Manager for Video-based Safety, you will define, own, and execute the product strategy involving AI/ML models to enhance safety outcomes, collaborating with engineering and design teams.

Manage member relationships and assist clients in achieving goals through strategic planning and customized engagement. Provide executive-level guidance and participate in client activities to ensure retention and growth.

The role involves developing features for GitLab's AI-powered platform, advocating for quality improvements, and collaborating within a remote team.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

As a Fullstack Engineer, you will build AI-assisted developer tools in GitLab Duo, working with TypeScript and Kotlin across various platforms, collaborating closely with engineering teams.

The Lead AI Engineer guides a team in delivering AI solutions, oversees technical execution, and bridges business needs with technology.

Pre-Sales AI Solution Designers collaborate with teams to propose AI-driven solutions, design architectures, present capabilities, and assist with proposals, ensuring compliance and ethical standards in AI development.

As an Applied AI Solution Engineer, you will design, build, and deploy AI applications, interact with clients, and solve technical problems, leading teams and ensuring solution quality.

The Senior Applied AI Solution Engineer will design, develop, and lead AI applications, mentor engineers, and communicate with clients to align AI strategies with business objectives.

The Regional Sales Manager will lead a sales team to acquire new corporate clients and expand market share through strategic planning and execution.

Lead and manage a team focused on machine learning infrastructure, guiding development and ensuring high-quality delivery while fostering a collaborative and inclusive environment.

As a Software Engineer, AI, you'll train large-language models by comparing, ranking, and refining AI-coded snippets, while providing feedback for reinforcement learning.

Train large-language models (LLMs) by evaluating, ranking, repairing, and refactoring code. Provide detailed feedback and maintain the RLHF pipeline.

The Software Engineer will analyze, rank, and improve AI-generated code, providing feedback for reinforcement learning in a remote role.

The role involves training large-language models to improve code generation, reviewing and editing AI-generated code, and providing feedback for reinforcement learning refinement.

As a Software Engineer, you will help train large-language models in code evaluation and training, focusing on Ruby, with an emphasis on code review and improvement.

Responsible for training large-language models by reviewing, ranking, and improving AI-generated code. Requires strong software engineering skills.

Train large-language models to improve code quality by comparing snippets, providing feedback, and refining AI-generated code. Requires strong communication skills and attention to detail.