Jobs at Faculty (faculty.ai)

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.

Leads complex Defence and national security programmes spanning AI strategy, R&D, digital product development, and deployment. Oversees portfolios, develops growth proposals, mentors teams, manages senior military and civilian relationships, and advises clients on AI implementation and operational outcomes. Requires substantial Defence experience, transformation programme leadership, digital product delivery, commercial consulting expertise, and UK Security Clearance eligibility. The role involves working onsite with UK customers two to four days weekly and travelling throughout the UK when required.

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.

Build and deploy production-grade machine learning software, tools, and infrastructure for high-stakes AI safety and national security applications. The role involves creating scalable ML solutions, evaluating frontier AI models, making architectural decisions, advising clients, and collaborating with engineers, data scientists, commercial teams, and research labs. Candidates should have strong Python and software engineering skills, cloud and containerization experience, ML lifecycle knowledge, and familiarity with LLM applications and AI safety evaluation.

Lead the architecture, development, deployment, and scaling of secure, production-grade machine learning systems and AI safety evaluations. The role includes technical scoping, cloud infrastructure design, client advising, cross-functional collaboration, engineering standards, and mentoring. Candidates need strong Python and software engineering expertise, experience with LLM applications, TensorFlow or PyTorch, cloud platforms, cybersecurity, Docker, Kubernetes, and full ML lifecycle operations.

Build and scale AI-powered systems in collaboration with data scientists and product managers. Develop reliable frontend and backend architectures, data pipelines, and cloud infrastructure. Deploy machine learning outputs into production-ready applications, provide technical expertise to customers, and manage the full software lifecycle while balancing rapid prototyping with long-term maintainability.

Leads Faculty’s brand strategy, marketing planning, narrative development, and execution. Owns brand positioning, foundational assets, digital platforms, campaigns, creative, events, and content quality. Partners with marketing, communications, editorial, technical, and executive leaders to align brand activity with commercial strategy. Builds and coaches a multidisciplinary team, drives international brand expansion, and promotes AI-native marketing practices while maintaining a distinctive, high-quality brand standard.

Owns end-to-end recruitment for commercial and central-function roles, from scoping and sourcing through interviews, offers, and onboarding. Advises hiring managers, builds diverse passive-talent pipelines, designs structured assessments, analyzes recruitment metrics, supports inclusion initiatives, and represents the employer brand at industry events. The role contributes to hiring strategy and delivers a high-quality, inclusive candidate experience in a fast-growing AI organization.

Build and deploy production-grade machine learning software, tools, infrastructure, and scalable reusable solutions. Collaborate with engineers, data scientists, commercial leads, clients, and partners to solve complex challenges. Lead technical scoping, architecture, ML deployment standards, and customer advisory activities while translating technical concepts for non-technical stakeholders.

Lead delivery of AI safety evaluations, frontier model red teaming, and safeguard testing projects. Build trusted relationships with clients, data scientists, and technical partners; translate AI safety research into strategic solutions; develop proposals to grow AI safety and governance work; advise clients on implementation; and mentor team members across a high-impact portfolio.

Build and deploy production-grade machine learning software, tools, infrastructure, and scalable solutions. Collaborate with engineers, data scientists, commercial leads, clients, and partners to solve complex challenges. Lead technical scoping, architecture decisions, and ML deployment standards while advising technical and non-technical stakeholders on production AI systems.

Build and scale AI-powered systems in cross-functional teams. Responsibilities include developing scalable frontend and backend architectures, deploying machine learning outputs into production, building data pipelines and cloud infrastructure, and advising customers on complex technical requirements. The role requires ownership across the application lifecycle, balancing rapid prototyping with maintainability, and collaborating with data scientists and product managers.

Supports the delivery of bespoke AI transformation projects for retail and consumer clients. Responsibilities include managing workstreams, gathering stakeholder and user needs, conducting open-source analysis, translating operational requirements into user stories, designing and testing workflows, collaborating with engineering teams, and contributing to strategy and internal capability development.

Lead the design, development, and deployment of scalable, production-grade machine learning systems for diverse clients. Define ML deployment standards, make architectural decisions, collaborate with technical and commercial teams, advise customers, and mentor junior engineers. The role requires strong Python and software engineering expertise, experience operationalizing TensorFlow or PyTorch models, cloud architecture, security, infrastructure, Docker, and Kubernetes.

Supports the scoping, development, and delivery of bespoke AI projects for pharmaceutical clients. Responsibilities include managing workstreams, understanding stakeholder needs, conducting open-source analysis, translating operational requirements into user stories, designing and testing workflows, collaborating with engineering teams, and contributing to strategy and thought leadership.

The Head of Research and Development for AI Safety will lead a research team focused on AI safety, drive the research agenda, manage publications, and influence AI safety strategies with industry impact.