Jobs at Faculty (faculty.ai)

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.

Leads technical strategy, architecture, capability testing, and deployment of secure AI and machine learning systems. Builds scalable ML infrastructure, orchestration tools, and multi-agent safety frameworks; establishes standards for reliable and ethical AI deployment. Advises senior clients, translates complex technical risks, mentors engineers, and partners with research teams on model evaluations, governance, and production-grade AI solutions.

Oversee an education-sector portfolio of AI and technology projects, ensuring high-quality delivery, commercial viability, risk management, and client satisfaction. Build trusted customer relationships, lead project meetings, develop proposals and sales opportunities, mentor cross-functional teams, and advise clients on AI strategy. The role also requires understanding education policy and technology, managing project scope and margins, and translating complex technical challenges into practical business solutions.

Leads complex AI and digital transformation programmes for UK Government and international public-sector clients. Responsibilities include overseeing portfolios spanning strategy, R&D, and product delivery; developing proposals and growing the public-sector business; advising senior customers on AI strategy and implementation; managing client relationships; mentoring teams; and ensuring high-quality outcomes. Requires public-services experience, GDS-style product delivery, AI and ML understanding, technical credibility, consulting delivery expertise, and strong leadership in ambiguous, high-stakes environments.

Leads high-impact AI and data science projects for retail and consumer clients. Defines technical approaches, translates business problems into scalable solutions, designs software architectures, manages delivery timelines, builds client relationships, mentors data scientists, and promotes responsible AI practices. The role also contributes to business development, hiring, project scoping, and the development of production-grade machine learning and agentic AI systems.

Manage workstreams for bespoke AI transformation projects with pharmaceutical and life sciences clients. Responsibilities include understanding stakeholder needs, conducting open-source analysis, translating operational requirements into user stories, designing and testing workflows, collaborating with engineering teams, and contributing to thought leadership. The role requires prior technical transformation consulting experience, life sciences exposure, client relationship skills, analytical communication, and familiarity with Agile and iterative delivery.

Design, build, and deploy production-grade ML systems and reusable infrastructure. Collaborate with cross-functional teams, lead technical scoping and architecture, define deployment standards, and advise clients on ML feasibility and delivery.

Lead technical direction and delivery of large-scale, production ML systems. Design and implement scalable model deployment, lifecycle management, CI/CD, APIs, testing and versioning frameworks. Oversee multiple complex workstreams, justify architectural decisions to stakeholders, coach engineers, and recommend new AI technologies to maintain competitive advantage in high-risk environments.

Leads strategic growth and complex AI transformation programs for telecommunications, media, and technology clients. Builds C-suite relationships, expands accounts, shapes commercial pitches, oversees multidisciplinary delivery teams, and develops thought leadership. The role combines executive advisory, revenue ownership, technical credibility, and practice leadership to deliver secure, reliable AI deployments and strengthen Faculty’s TMT market position.

Lead technical direction and delivery of large-scale AI/ML platforms. Architect microservices and shared libraries, align ML, data science and DevOps, coach distributed engineers, define production readiness, and drive hiring and standards across software engineering.

Lead the architecture, development, and deployment of secure, scalable machine learning systems and AI safety evaluation tools. Design production-grade ML infrastructure, establish deployment standards, advise clients, collaborate with technical and commercial teams, and mentor engineers. The role focuses on operationalizing LLM applications, supporting frontier AI model testing, and delivering robust systems for high-stakes national security and AI safety missions.