Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- Experience integrating generative AI tools or Large Language Model (LLM) interfaces into workflows.
Preferred qualifications:- Master's degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
As a part of Google Pics, an upcoming AI-powered visual editor seamlessly integrated across Google Workspace to bring Gen-AI photo and design capabilities directly onto the canvas. In this role, you will work with the advanced image-understanding capabilities of GemPix and Gemini to empower users to intuitively edit, transform, and generate images across all Workspace surfaces. In this role, you will be executing against a 2026 roadmap, building on limited testing and consumer experiments ahead of a General Availability (GA) launch in 2026. You will join a team that requires executing at a fast pace against a comprehensive roadmap of model capabilities.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $301000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities- Build both human-powered and Large Language Model (LLM)-powered automated evaluation systems to assess model performance.
- Establish clear metrics to measure aspects like grounding, coherence, safety, and helpfulness.
- Utilize platforms and tools to efficiently run evaluations across different models and datasets.
- Provide actionable insights from evaluations to improve model quality, often in collaboration with research, and cross-functional teams.
- Create tools and systems that make the evaluation process more efficient and effective.
