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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.

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 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.

Lead the technical direction and delivery of complex machine learning and software projects. Architect scalable production systems, define roadmaps, manage high-risk workstreams, develop reusable libraries, mentor and hire engineers, evaluate technologies, estimate work, and advise customers and stakeholders. The role combines advanced software and ML engineering with team leadership, technical coaching, client delivery, and solution architecture.

Lead AI safety projects for frontier labs, developing evaluation frameworks, scientific experiments, and supporting software architectures. Mentor technical teams, shape project scope and feasibility with commercial partners, advance AI evaluation research, and ensure rigorous scientific and technical standards throughout delivery.