Waymo
Senior Staff Engineer, Agentic AI Engineering
$251K–$310K
Mountain View, CA, USA +3 · Senior Staff · Full-time · Posted Jul 9, 2026
- Location
- Mountain View, CA, USA
San Francisco, CA, USA
Mountain View, California, United States
San Francisco, California, United States - Experience
- Typically 7+ years in software engineering; the posting also requires a proven technical leadership track record.
- Education
- Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience.
About the job
Designs and deploys agentic AI systems and scalable evaluation methods to improve how Waymo identifies and addresses long-tail autonomous-driving problems.
What you'll do
- Build and deploy production-grade agentic AI systems for complex evaluation challenges.
- Develop automated evaluation frameworks and objective metrics for agent accuracy, safety, and reliability.
- Automate the creation and scaling of difficult evaluation problems.
What they ask for
- Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience.
- Track record of designing, building, evaluating, and deploying agent-based AI solutions into production.
- Deep AI/ML expertise, including hands-on experience with autonomous agents, multi-agent orchestration, and LLMs integrated with external tools/APIs.
- Typically 7+ years in software engineering, significantly focused on architecting and building large-scale, complex systems.
Nice to have
- PhD in Computer Science or a related field specializing in AI/ML or a related area.
- Experience with agentic frameworks, knowledge graphs, reasoning engines, C++, production-grade services, autonomous-vehicle evaluation, or Google's/Waymo's internal AI/ML platforms.
Pay
$251,000 — $310,000 USD (base salary)
Tools
Artificial Intelligence, Machine Learning, LLMs, external tools/APIs, C++, agentic frameworks, knowledge graphs, reasoning engines
AI focus
Build production agentic AI systems and automated evaluation frameworks, including autonomous and multi-agent systems and LLM integrations, to measure agent accuracy, safety, and reliability and scale autonomous-driving evaluation.
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