Staff ML Engineer: Search AIGC Quality

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

Job summary

Google's staff ML engineer role focuses on Search AIGC quality for Discover and Notifications. You will own evaluation frameworks, context engineering, and content understanding to ensure factual, fresh, coherent, and appropriately toned AI-generated content for billions of users worldwide.

You will build and optimize agentic loops, integrate LLM quality with downstream signals, and address system bottlenecks for scalable, low-latency serving across Google surfaces.

Qualifications

• Bachelor's degree or equivalent practical experience is required.
• 8 years of software development experience.
• 5 years of ML, recommendation systems, NLP or related field.
• Experience building offline/online quality evaluation frameworks for LLMs.
• Experience integrating generative AI tools or LLM interfaces into workflows.

Responsibilities

• Build advanced AIGC quality frameworks and agentic flows for proactive surfaces.
• Develop AI-generated content through context engineering and feedback loops.
• Conduct advanced quality evaluations and leverage downstream signals.
• Resolve system bottlenecks to optimize low-latency serving of AIGC content.
• Navigate ambiguity and influence the broader ML strategy.

Skills

Decision making

Ambiguity resolution

Stakeholder influence

Domain expertise

Education

Bachelor’s degree or equivalent practical experience

Master’s degree or PhD in Engineering, Computer Science, or a related technical field

Tools

Quality evaluation frameworks for LLMs

Integrating generative AI tools/LLM interfaces into workflows

Low-latency serving optimization

RLHF integration

Job description

Google's staff ML engineer role focuses on Search AIGC quality for Discover and Notifications. You will own evaluation frameworks, context engineering, and content understanding to ensure factual, fresh, coherent, and appropriately toned AI-generated content for billions of users worldwide.

You will build and optimize agentic loops, integrate LLM quality with downstream signals, and address system bottlenecks for scalable, low-latency serving across Google surfaces.