Staff ML Engineer, AI Discover & Feed Recommendations

Google LLC; Mountain View (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google LLC is seeking a Staff ML Engineer for the Search Discover Feed Recommendation team in Mountain View, CA. The role focuses on building AIGC quality frameworks and agentic flows to deliver high-quality AI-generated content across Discover and Notifications.

You will work on context engineering, evaluation loops, and integration of LLM-based tools into workflows, impacting billions of users globally. Strong leadership and cross-team collaboration are essential.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience in ML, recommendation systems, NLP, or related field.
• Experience building offline/online quality evaluation frameworks for LLMs (e.g., LLM-as-a-judge, RLHF, DPO).
• Experience integrating generative AI tools or LLM interfaces into workflows.

Responsibilities

• Build advanced AI-generated content (AIGC) quality frameworks and agentic flows for Discover and Notifications.
• Develop AI-generated content through context engineering and agentic feedback loops.
• Conduct advanced quality evaluations and leverage downstream signals to improve AIGC performance.
• Resolve system-level bottlenecks in recommending and serving AIGC content for low-latency surfaces.
• Influence broader ML strategy and drive technical innovation for AIGC quality.

Skills

Software development

Machine learning

Recommendation systems

NLP

LLM integration

Quality evaluation

Education

Bachelor’s degree or equivalent practical experience

Master’s degree or PhD (preferred)

Tools

RLHF

AIGC frameworks

Job description

Google LLC is seeking a Staff ML Engineer for the Search Discover Feed Recommendation team in Mountain View, CA. The role focuses on building AIGC quality frameworks and agentic flows to deliver high-quality AI-generated content across Discover and Notifications.

You will work on context engineering, evaluation loops, and integration of LLM-based tools into workflows, impacting billions of users globally. Strong leadership and cross-team collaboration are essential.