Jobs at Faculty (faculty.ai)

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.

Lead the technical direction and delivery of complex machine learning platforms in high-risk environments. Design scalable production ML systems, deployment infrastructure, APIs, testing frameworks, model versioning, and CI/CD pipelines. Define architecture and technical roadmaps, oversee multiple workstreams, guide technology adoption, and manage and coach engineers. The role requires expertise in cloud-native ecosystems, Kubernetes, TensorFlow or PyTorch, agentic systems, and production model operations.

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

Oversee a portfolio of AI delivery projects, ensuring quality, timeliness, commercial viability, and client satisfaction. Build trusted customer relationships, manage risks and scope, lead project meetings, develop proposals, identify account-growth opportunities, and mentor cross-functional teams. Advise clients on AI strategy and solutions while translating technical concepts into business value. Apply structured problem-solving and Agile, Scrum, or Kanban methodologies in a consulting environment.

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.

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.

Build and optimize cloud platform infrastructure, multi-environment architectures, Infrastructure as Code, Kubernetes clusters, and automated deployment solutions. Own security controls, monitoring, logging, incident response, and root cause analysis for production environments. Collaborate with software and ML engineers on production readiness, develop Python platform tooling, and mentor engineers through reviews and best-practice sharing. The role may support government clients and require UK Developed Vetting eligibility and occasional onsite work.

Design and scale Faculty’s Fellowship learning experience across regions and cohorts. Responsibilities include developing modular curricula, mentor tools, train-the-trainer programs, delivery standards, secondment pipelines, and continuous improvement reporting. The role partners with SMEs, facilitators, operations, business-unit leaders, and People teams while delivering broader capability projects and evaluating learner progression, mentor impact, and program quality.

Lead the architecture, development, and deployment of production-grade AI, machine learning, software, and infrastructure systems. Establish scalable ML deployment standards, collaborate with engineering, data science, product, and commercial teams, advise clients, and mentor junior engineers. The role requires strong technical leadership, cloud expertise, client-facing communication, and the ability to deliver robust systems in a fast-paced environment.

Lead high-impact AI projects for energy, transition, and environment clients. Define data science approaches, design robust software architectures, develop bespoke algorithms, mentor data scientists, assess technical feasibility, plan projects, estimate timelines, and lead delivery teams. Build client relationships, translate business problems into mathematical frameworks, contribute to thought leadership, and ensure rigorous best practices throughout the project lifecycle.

Leads private equity AI due diligence, market scans, AI maturity benchmarking, portfolio value creation, and internal AI use cases. Translates technical risks, data assets, IP, architecture, AI economics, and disruption potential into investment insights. Manages client relationships, project delivery, commercial viability, proposals, business development, and cross-functional teams while advising PE sponsors and portfolio executives on AI strategy and value creation.

Lead technical direction for complex data science and machine learning projects, design production-grade solutions, define roadmaps, manage technical risk, support sales engagements, advise customers, establish best practices, and mentor data scientists and cross-functional teams.

Lead the technical direction and delivery of complex AI and software projects. Architect and maintain scalable production systems, define project roadmaps, oversee high-risk delivery, develop shared engineering libraries, and guide architectural decisions. Lead hiring, mentor engineers, advise customers, estimate workstreams, recommend technologies, and coach technical and non-technical stakeholders.