About this role
Google's projects, like our users, span the globe and require managers to keep the big picture in focus while being able to dive into the unique engineering challenges we face daily. As a Technical Program Manager at Google, you lead complex, multi-disciplinary engineering projects using your engineering expertise. You plan requirements with internal customers and usher projects through the entire project lifecycle. This includes managing project schedules, identifying risks and clearly communicating them to project stakeholders. You're equally at home explaining your team's analyses and recommendations to executives as you are discussing the technical trade-offs in product development with engineers.
Using your extensive technical and leadership expertise, you manage projects of various size and scope, identifying future opportunities, improving processes and driving the technical directions of your programs.
Artificial intelligence will be one of humanity’s most transformative inventions. At DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Minimum qualifications:
- Bachelor’s degree in Computer Science, Engineering, Biology, Chemistry, Physics, a related technical/scientific field, or equivalent practical experience.
- 7 years of experience in technical program management, software engineering, or research engineering.
- 3 years of experience partnering with research or engineering leads to delivering AI/ML programs, evaluation suites, or safety frameworks.
Preferred qualifications:
- Master’s degree or PhD in Computer Science, Machine Learning, Biosecurity, Chemistry, or related scientific discipline.
- Experience in frontier AI safety, red-teaming methodologies, threat modeling, or dangerous capability evaluations for large language models (LLMs) and autonomous agents.
- Experience in a frontier AI lab, national laboratory, government defense, or biosecurity research institution.
- Lead E2E engineering delivery of CBRNE evaluation pipelines, mitigations, and red-teaming across the model lifecycle; partner with the Portfolio Lead and Program Manager (PM) to ensure critical priorities are addressed while translating ambiguous scientific milestones into technical roadmaps and project plans.
- Partner closely with engineers, scientific (SMEs), and the PM to translate complex safety risks and requirements into clear, measurable engineering goals, while driving strategic planning and compute allocation.
- Build and guide effective cross-functional partnerships across disciplines, including modeling, infrastructure, evaluation, and release teams, to ensure alignment on objectives and resolve blockers, serving as the operational bridge.
- Implement frameworks include OKRs, stand-ups, and agile methodologies to track progress, pre-empt risks, and optimise workflows while fostering a collaborative, interdisciplinary team culture.
- Distill complex technical updates into clear, actionable insights for audiences and executive leadership.