Software Engineer III, Agent Quality and Efficiency Optimization

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. Poland: zł280000 - zł287000 (PLN) + 15% bonus target + equity + benefits Learn more about[benefits at Google .

Responsibilities

• Design, build, and ship agent optimization systems that turn multiple sources of insights (like eval results, production traces, etc.) into persistent improvements, auto-formed skills, recipes for prompt and tool tuning, per-task cost and quality telemetry, smart model routing, durable and deferred execution, and continuous optimization, across the Agent Development Kit and Vertex AI serving stack.
• Measure quality, cost, and latency per task, and use that data to drive engineering work.
• Shape the team's optimization roadmap, and help set technical direction for how enterprise agents get better and cheaper over time.
• Build benchmark optimization techniques on both open source benchmarks and real first-party and enterprise agent workloads to prove quality and efficiency wins hold up together.