The US base salary range for this full-time position is $187,100 - $253,000 + 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 enhance serving solutions for Gemini models, tailoring configurations to meet diverse client needs and testing for optimal performance.
- Develop new infrastructure to support advanced capabilities, such as large-scale streaming and specialized audio logic within the orchestration framework.
- Guarantee the quality of Gemini models in production by triaging system issues, debugging code, and implementing robust monitoring systems.
- Collaborate with peers and stakeholders through design and code reviews to establish and maintain best practices in software development.
- Drive the long-term technical vision and roadmap for your team, evolving strategies to meet future requirements and infrastructure needs.
Minimum qualifications:
- Bachelor’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field and 3 years of experience in the job offered or in a Software Engineer-related occupation.
- Position requires 3 years of experience in the following: Algorithm design and data structure selection for system scalability; Software design and systems architecture for performance and reliability; Productionizing large language and multimodal models or ML infrastructure development for model deployment, evaluation, and optimization; Performance engineering for software optimization; and Software testing across the development lifecycle including validity, verification, performance, and reliability.