Tech Lead Manager, Model Performance, DeepMind

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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience in a people management, supervision/team leadership role.
• 5 years of experience in a technical leadership role and overseeing projects.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 5 years of experience working in a complex, matrixed organization.

Responsibilities:

• Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and decision-making across teams.
• Set clear expectations with individuals based on their level and role and aligned to the broader organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching.
• Develop the long-term technical vision and roadmap within, and often beyond, the scope of your teams. Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
• Oversee systems designs within the scope of the broader area, and review product or system development code to solve ambiguous problems.
• Drive technical project strategy, lead large-scale ML infrastructure optimization, and oversee the design and implementation of solutions across multiple specialized ML areas.