Top Tech Jobs & Startup Jobs in London

Lead high-impact AI projects for life sciences clients, defining data science approaches, designing software architectures, mentoring data scientists, and ensuring rigorous delivery. Build client relationships, assess technical feasibility, shape project scope, contribute to thought leadership, and develop innovative algorithms using Python, NumPy, Pandas, and PyTorch across core data science paradigms including NLP, supervised learning, unsupervised learning, and validation.

Design and deliver end-to-end data science solutions for complex client problems, including algorithm selection, statistical modeling, deep learning, validation, and production-grade model architecture. Own scalable and mathematically rigorous model components, translate ambiguous requirements into technical frameworks, manage delivery risks, and mentor junior data scientists. Communicate with customers and promote ethical, transparent, peer-reviewed AI development.

Lead complex data science and machine learning projects from technical scoping through production delivery. Set technical direction, architect reliable solutions, define roadmaps, manage technical risk, and oversee multidisciplinary workstreams. Lead client engagements, support commercial opportunities, establish technical best practices, and mentor data scientists and managers. The role also requires influencing engineering, infrastructure, commercial, and business-unit initiatives while delivering practical outcomes under commercial pressure.

Lead the design, development, and deployment of scalable, production-grade machine learning systems. Define ML deployment standards, make architectural decisions, collaborate with technical and commercial teams, advise clients, and mentor engineers. The role requires expertise across the ML lifecycle, software engineering, cloud infrastructure, containerization, orchestration, and responsible AI delivery.

Build and deploy production-grade machine learning software, tools, and infrastructure for diverse clients. Develop scalable reusable ML solutions, collaborate with engineers and data scientists, lead technical scoping and architecture decisions, establish deployment standards, and advise technical and non-technical stakeholders. The role requires expertise across the ML lifecycle, Python software engineering, cloud infrastructure, containerization, orchestration, and core machine learning concepts.

Coordinate Faculty’s learning and development operations, including new-hire onboarding, learning calendars, workshop logistics, platform administration, content updates, communications, and learner support. Manage Sana enrollments, content tagging, troubleshooting, surveys, and version control. Produce learning dashboards and insights while using AI tools to improve administrative efficiency. The role requires strong organization, stakeholder coordination, communication, digital confidence, and regular attendance at the London office.

Supports financial planning and strategic decision-making for UK and international growth. Responsibilities include financial modeling, scenario planning, budgeting, forecasting, profitability analysis, investment cases, performance monitoring, and actionable recommendations. The role partners with senior stakeholders across commercial, delivery, and operational teams while improving financial reporting, forecasting, data analytics, automation, and access to financial insight.

Build scalable, maintainable, and well-tested React applications that visualize AI and machine learning insights. Collaborate with data scientists, data engineers, and designers to deliver client-facing products. Lead frontend feature planning, deployment infrastructure, technical standards, reusable component libraries, mentoring, training, and onboarding while serving as the frontend engineering authority for the team.

Leads upstream AI safety and evaluation initiatives for Frontier Labs, directing project teams, designing scientific experiments, architecting software infrastructure, mentoring data scientists, and advising senior clients. The role combines quantitative research, rigorous methodology, technical leadership, commercial scoping, and thought leadership through novel evaluation methods, speaking, and open-source research.

Lead technical strategy, architecture, capability testing, and deployment of secure, scalable machine learning systems. Build production-grade ML infrastructure, establish safe and reliable AI standards, guide multi-agent and frontier-model evaluations, advise senior clients, mentor engineers, and collaborate with Frontier Labs on AI safety, governance, and sandboxing frameworks.