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.
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.