Minimum qualifications:

• Bachelor’s degree in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning or a related field and 2 years of experience in the job offered or in a Technical Program Manager-related occupation.
• Position requires 2 years of experience in the following: Planning and program management for complex technical products across the full product lifecycle from prototype to end-of-life or product development lifecycle management. Applying technical judgment to assess engineering problems and shape technical directions in machine learning systems or technical problem assessment. Knowledge of machine learning, GenAI, and LLM systems, infrastructure, and product evaluations. Stakeholder management with engineering and product management teams to deliver technical products or cross-functional team alignment. Managing organizational change in a rapidly moving environment using rollout plans and impact analysis or change implementation

About the job

The US base salary range for this full-time position is $184,000 - $198,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google

Position reports to the Google Mountain View, CA office & may allow for a hybrid schedule as per Google policy.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google 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.

Responsibilities

• Monitor critical evaluation processes and communicate status across the organization
• Define and automate key performance metrics to track evaluation success
• Develop data pipelines and reporting dashboards to provide program visibility
• Collaborate with research and engineering partners to align on priorities and drive progress
• Manage the full program lifecycle from scope definition to execution and governance. Apply technical judgment to improve evaluation infrastructure and solve complex problems.