About the job
The Agent Quality team defines, measures, and advances long-horizon, tool-using capabilities for Google’s frontier agents. We build foundational evaluation systems and data flywheels for Gemini Spark—Google’s flagship agent for deep research, multi-step problem solving, and workspace integration. Our work encompasses pioneering environment creation (including contexts, autoraters, human evaluations), developing advanced diagnostic tools for agent improvement, and harvesting trajectories and reward signals into SFT and RL post-training to recursively improve core Gemini models.
Responsibilities
Design, build, and scale realistic agent environments and task suites.
Research and develop Agentic AutoRaters (AR), calibrate them against human evaluation, and benchmark agent capabilities against industry-leading frontier models.
Enable agents to automatically generate and iterate on verifiers (such as automated test cases), facilitating effective exploration and iterative problem-solving.
Develop trajectory analysis frameworks and diagnostic tooling to identify root-cause agent failure modes (e.g., passivity, hallucination, or brittle tool execution), and automatically optimize agent harnesses to drive continuous self-improvement.
Harvest complex multi-turn interaction trajectories into high-quality datasets and reward signals to power SFT and RL flywheels for frontier Gemini models.
Qualifications
Minimum
2 years of experience with agentic AI workflows, frameworks, and approaches.
1 year of experience with machine learning and deep learning.
Experienece in software engineering and cloud-based development.
Experience with Python, TensorFlow, PyTorch, or similar ML frameworks.
Preferred
In-depth knowledge of machine learning algorithms, including supervised learning and reinforcement learning.