Minimum qualifications:
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- 5 years of experience in technical program management, software engineering, or research engineering.
- 3 years of experience partnering with research or engineering leads to deliver AI/ML programs, evaluation suites, or safety frameworks.
Preferred qualifications:
- Experience in frontier AI safety, red-teaming methodologies, threat modeling, or dangerous capability evaluations for large language models (LLMs) and autonomous agents.
- Knowledge or active interest of AI Ethics, AI Safety, Public Policy etc.
- An understanding of model evaluation methodologies for determining the frontier risks of modern AI models; see Google's FSF and similar documents at other AI labs.
- Strong analytical and problem-solving skills with a demonstrated ability to optimize complex technical infrastructure, compute scheduling, and data-protection protocols for sensitive dataset.
About the job
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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits
Learn more about
benefits at Google.
Responsibilities
- Partner with research/engineering leads and SMEs to translate safety risks into measurable technical goals, driving strategic planning and compute resource allocation.
- Build and guide effective cross-functional partnerships across modeling, infrastructure, evaluation, governance, and public policy teams to resolve blockers and shape regulatory requirements.
- Implement robust program management frameworks (OKRs, stand-ups, retrospectives, dashboards, decision logs) to track progress, preempt risks, and optimise workflows while fostering a collaborative, interdisciplinary team culture.
- Distill complex technical updates into clear, actionable insights for executive leadership and external partners.
Manage program budgets, forecasting spend, purchase order reconciliation, and strategic resource allocation.
- Collaborate with leadership on cross-industry ‘horizon scanning’ to identify and maintain visibility of current and future external and internal testing requirements from regulators, government bodies, and wider industry standards to inform program strategy.
