Senior Software Engineer

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.

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.