Senior Staff Software Engineer, Agent Data Quality, DeepMind

About the job

At Google DeepMind our mission is to build the world's first general-purpose learning agent. Central to this mission is the complex task of measuring the intelligence of our prototypes. As a Software Engineer, you will be working with the cutting edge AI agents developed by our exceptional team of Machine Learning and Neuroscience research scientists. Your responsibilities will include everything from creating systems for agent testing using 2D and 3D games to developing test problems within physics simulators. You will create graphical visualization of results, build competitive agent leaderboards and test new algorithms on robots. To succeed in this role you will need to have a strong foundation in software engineering and enjoy working on a wide range of challenging problems within a mission-driven team.

In this role, you will work cross-functionally with research engineers, data scientists, agent platform, and model teams to ensure we have the best quality agents for GenAI model improvement and product developments. This role is fast-paced and high-exposure, requiring a top-notch back-end data engineer capable of making high-stakes architectural judgments during a period of significant organizational evolution. To achieve this, you will build agent data processing pipelines for user sentiment and behavior understanding, and develop the experiment framework to evaluate performance and provide feedback to agent and model development.

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 a highly efficient agent data processing pipeline for user sentiment and behavior understanding and provide data insights and visualization systems.
• Build the experiment framework to evaluate agent and model performance to provide realtime feedback for the agent and model development.
• Construct rigorous quantitative benchmarks and automated evaluation frameworks (including LLM-as-a-judge) to measure agent capabilities in reasoning, planning, and tool use.
• Analyze agent behavior to identify failure modes, edge cases, and performance bottlenecks, turning these insights into actionable improvements.