Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 5 years of experience developing C backend infrastructure components within distributed systems.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- Experience with Large Language Models (LLMs) and Generative AI concepts (e.g., prompt engineering, retrieval-augmented generation (RAG)).
- Experience with model quality optimization using methods such as loss analysis or quality hillclimbing.
Preferred qualifications:- 5 years of experience with data structures and algorithms.
- 1 year of experience in a technical leadership role.
- Experience working in fast-paced, startup-like environments with rapidly changing priorities.
- Experience in data analysis, experimental design, and statistical methods.
- Experience with common ML frameworks (e.g., TensorFlow, JAX, PyTorch).
- Familiarity with user metrics logging and A/B testing for software products.
About the jobThe Geo team is focused on building the most accurate, comprehensive, and useful maps for our users, through products like Maps, Earth, Street View, Google Maps Platform, and more. Every month, more than a billion people rely on Maps services to explore the world and navigate their daily lives.
The Geo team also enables developers to use the power of Google Maps platforms to enhance their apps and websites. As they plot a course for the future of mapping, they are solving complex computer science problems, designing beautiful and intuitive product experiences, and improving our understanding of the real world.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Build context engineering pipelines, agentic workflows with tool usage, and robust evaluation frameworks.
- Analyze model behavior, creating high-quality evaluation datasets to identify weaknesses and guide performance improvements.
- Enhance serving and evaluation infrastructure to power new user-facing features.
- Act as a technical bridge between engineering, product, UX, and research teams to translate user needs into valuable features.
- Analyze user metrics and model outputs to enhance personalization and overall system helpfulness.
