Staff ML Software Engineer, Search Ecosystem
Mountain View, CA, USA · Staff · Full-time · Posted Oct 30, 2026
- Experience
- 8 years in software development, including 5 years testing and launching software products and 3 years in software design and architecture; 5 years building ML systems and infrastructure. Preferred: 3 years in technical leadership and 3 years in a complex, matrixed organization.
- Education
- Bachelor’s degree or equivalent practical experience; preferred: Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
About the job
Builds scalable, secure platforms and infrastructure for acquiring content, evaluating AI systems, and improving models used in Google Search.
What you'll do
- Develop frameworks to evaluate AI components and data, analyze results, and drive performance improvements.
- Design and maintain specialized infrastructure on top of LLMs, and help set technical strategy.
- Lead technical projects from ideation through production in an ambiguous, fast-paced environment.
What they ask for
- Bachelor’s degree or equivalent practical experience.
- 8 years of software development experience, including 5 years testing and launching software products and 3 years in software design and architecture.
- 5 years building ML systems and infrastructure for training, evaluation, deployment, or serving.
- Experience integrating generative AI tools or LLM interfaces into workflows, and experience with data-processing software, strategies, or algorithms.
Nice to have
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field; technical leadership experience; or experience in a complex, matrixed organization.
- Full-stack development, back-end distributed infrastructure, Java, Python, or C++; experience integrating generative AI tools and understanding AI architecture.
Pay
$207000 - $300000 USD base salary
Tools
LLMs, Hadoop, Spark, Beam, Hive, MapReduce, Flume, Java
AI focus
Build infrastructure for content acquisition and AI model training; evaluate AI components and data, improve model performance, and integrate generative AI tools and LLM interfaces.
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