Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Lead a team of engineers, driving career growth, team culture, and execution across data collection, evaluation generation, and unified benchmarks.
Act as Tech Lead for the Quality Flywheel, architecting systems for scalable data collection, realistic simulation environments, eval generation, and online hill climbing.
Direct software design and implementation for next-gen evaluation infrastructure (Eval Infra), tooling, and Gemini post-training systems adhering to architectural best practices.
Partner across teams to distill complex, ambiguous model and platform requirements into simplified, robust technical solutions.
Drive overarching testing and reliability architecture, advocating production stability, test environment improvements, and on-call operational support.
Minimum qualifications
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience in software engineering leadership in ML Infrastructure, ML, or product-focused AI.
5 years of experience with large scale distributed systems.
2 years of experience in a people management or team leadership role.
Experience with Generative AI lifecycle, including Agent frameworks or protocols (e.g., MCP, A2A), RAG, prompt engineering, or model evaluation systems.
Preferred qualifications
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
Demonstrated experience architecting or contributing to simulation frameworks, evaluation infrastructure, or enterprise quality platforms.
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience in software engineering leadership in ML Infrastructure, ML, or product-focused AI.
5 years of experience with large scale distributed systems.
2 years of experience in a people management or team leadership role.
Experience with Generative AI lifecycle, including Agent frameworks or protocols (e.g., MCP, A2A), RAG, prompt engineering, or model evaluation systems.
