AI Jobs in United Kingdom

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Contributors may design original computational biology problems that simulate real biology research workflows, create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and BioPython, ensure problems are computationally intensive and not solvable manually within reasonable time, develop problems requiring non-trivial reasoning chains in bioinformatics, systems biology, and molecular modeling, base problems on real research challenges or practical applications from biology practice, verify solutions using Python with standard computational biology libraries, and document problem statements clearly providing verified correct answers.

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Contributors may design original computational biology problems simulating real biology research workflows; create problems requiring Python programming using libraries such as Numpy, SciPy, and BioPython; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning in bioinformatics, systems biology, and molecular modeling; base problems on real research challenges or practical applications from biology; verify solutions using Python with standard computational biology libraries; document problem statements clearly and provide verified correct answers.

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Research and experiment on ways to specialize foundational models to agentic use cases, build and maintain data and training pipelines, keep up with latest research and be familiar with state of the art in LLMs, alignment, synthetic data generation, and code generation, design, analyze, and iterate on training, fine-tuning, and data generation experiments, write high-quality and pragmatic code, and work as part of a team by planning future steps, discussing, and communicating clearly with peers.

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The AI Productivity Engineer will take clear ownership of rapid AI adoption across the engineering organization by building AI-powered tools and systems that improve engineering productivity, reducing friction, automating repetitive tasks, and embedding intelligence into workflows. Responsibilities include identifying high-friction areas in engineering workflows, designing and building production-grade AI-powered developer tooling for coding, testing, PR reviews, and debugging, building contextual AI assistants using internal data and tools, exploring, prototyping, and productionizing AI solutions, automating workflows across platforms like GitLab, Jira, CI/CD, Slack, and observability tools, designing and operating internal AI services and orchestration layers, owning solutions end-to-end from discovery to iteration, working hands-on with engineering teams to remove friction and enable tool usage, and measuring success through adoption, impact, and tangible time saved for engineers. The role explicitly excludes building AI features for customer-facing products, speculative AI research without clear outcomes, acting as general internal support, and owning generic ML infrastructure unrelated to developer productivity.

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As a Partner AI Deployment Engineer focused on AWS, the role involves serving as the primary technical counterpart to AWS field leadership, shaping strategy, defining engagement models, and building scalable systems globally. Responsibilities include influencing joint account strategy and technical direction, leading technical strategy for large enterprise engagements, guiding customers from ideation through architecture design to production deployment, and acting as a technical decision-maker and escalation point. The role requires designing and communicating AI architectures using OpenAI and AWS services, building prototypes and reference implementations, establishing best practices for scalable and secure GenAI systems, and enabling AWS and partners through scalable technical motions such as workshops and playbooks. It also includes mentoring partner technical teams, scaling impact through GSIs, RSIs, and ISVs, collaborating cross-functionally with Alliances, Product, Engineering, GTM, and Enablement teams, delivering insights to inform product roadmaps, and contributing to internal knowledge systems and standards for the AI Deployment Engineering function.

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Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.

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Design and implement the infrastructure and tooling used by poolside researchers and engineers. Research and implement evaluations and benchmarks for base models and instruction following models. Collaborate with applied research and product teams to define meaningful metrics and evaluations that capture progress on real world software development skills. Work in a team setting to plan future steps, discuss, and communicate clearly with peers.

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Act as a whole-systems thinker by making meaningful system design decisions and owning the architecture of core platform components from initial design through production deployment. Develop secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance and low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the enterprise platform. Drive proactivity without red tape by clearly communicating changes, plans, and proposals to cross-functional teams without waiting for approval. Bridge the gap between backend services, infrastructure, and operations to ensure seamless deployments and scalable architecture.

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As an AI Product Manager at Air Apps, you will lead the product development lifecycle for AI-driven features, working closely with engineers, designers, and data scientists to develop, launch, and scale AI-driven solutions. Responsibilities include defining and driving the AI product roadmap to ensure alignment with business objectives and user needs; collaborating with cross-functional teams including engineering, design, and marketing to develop and launch AI-powered features; conducting market research and analyzing user feedback to identify opportunities for AI integration; working closely with data scientists and machine learning engineers to optimize AI models for accuracy, performance, and user impact; defining key performance indicators (KPIs) to measure success and iterating based on data-driven insights; staying up to date with AI trends, emerging technologies, and best practices to ensure product competitiveness; and ensuring ethical AI usage and compliance with data privacy regulations.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and incorporate them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.