Minimum qualifications:- PhD in Computer Science, Machine Learning, or a related quantitative field, or equivalent practical experience.
- 2 years of experience with machine learning frameworks such as JAX, Flax, or PyTorch
- Experience conducting research in reinforcement learning, tool use, or agentic systems.
Preferred qualifications:- Experience publishing research in reinforcement learning, reinforcement learning from human feedback, or tool-use algorithms at machine learning venues.
- Experience working with large-scale distributed training infrastructure and scaling experiments.
- Research experience in systems design, code complexity, or working with large-codebase environments.
- Experience developing simple, scalable solutions for complex, open-ended research problems.
Responsibilities - Drive the research process for large-scale agent post-training from hypothesis formulation to delivery in the Gemini model recipe.
- Design and execute ablation studies to validate research hypotheses and accelerate experimental feedback loops.
- Communicate research findings, progress, and outcomes to the broader team through visualizations and reports.
- Develop research infrastructure and utilities for data analysis and model evaluations using standard engineering practices.
- Collaborate with other research scientists and engineers to maintain a regular feedback and communication loop.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
