The US base salary range for this full-time position is $226,150 - $252,000 + 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.
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.
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.
Responsibilities
- Build and improve agentic AI features within Google products using the Gemini models;
- Develop quantitative evaluations of agent performance using Gemini models and related infrastructure;
- Triage and fix production issues encountered within the product and agent;
- Collaborate with peers and stakeholders through design and code reviews to establish and maintain best practices in software development; and
- Drive the short-term technical vision and roadmap for your team, evolving strategies to meet future requirements.
Qualifications
Minimum qualifications:
- PhD degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field and 1 year of experience in the job offered or in a Software Engineer-related occupation.
- Position requires 1 year of experience in the following: Go or other general-purpose programming language for software development; Algorithm design and data structure selection for system scalability; Software design and systems architecture for performance and reliability; Python for machine learning and evaluations; and Software testing across the development lifecycle.