Software Engineer III, Agent Quality and Efficiency Optimization · Google

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.

Design, build, and ship agent efficiency systems, per-task cost and quality telemetry, smart model routing, durable and deferred execution, and continuous optimization, into the Agent Development Kit and Vertex AI serving stack.

Measure where cost and latency go, and turn that data into targeted engineering work.

Shape the team's efficiency roadmap, and help set technical direction for how enterprise agents run in production.

Benchmark efficiency techniques on both open source benchmarks and real first-party and enterprise agent workloads to prove cost and latency wins hold up without regressing accuracy.

Partner with the team's evaluation engineers, model-serving teams, and product teams to close optimization loops on measured behaviour and land changes across Agent Platform.

Minimum qualifications:

Bachelor’s degree or equivalent practical experience.

2 years of experience with software development in one or more programming languages such as Go, C++ or Python, or 1 year of experience with an advanced degree.

2 years of experience with machine learning, large language models (LLMs), or applied AI.

Preferred qualifications:

Master's degree or PhD in Computer Science, Machine Learning, or a related technical field.

Experience building, deploying, or optimizing LLM applications or agentic frameworks (e.g., Agent Development Kit, LangGraph, LangChain).

Experience with one or more of the following: model serving optimization, inference caching, model routing or LLM cost and latency profiling.

Experience instrumenting, benchmarking, or evaluating ML systems in production.

Experience with technical leadership, mentoring engineers, or driving cross-team engineering initiatives.