About the job
The Discover Feed Recommendation team pioneers proactively recommending AI-generated content (AIGC) to fulfill user interests. As a Staff Machine Learning Engineer focusing on Search AIGC Quality, you will be responsible for AIGC quality control, topic identification, context engineering, and content understanding. You will build the agentic loops and evaluation frameworks needed to ensure factuality, freshness, coherence, and tone for AI-generated content that empowers billions of users across Google's proactive surfaces like Discover and Notifications.
Responsibilities
Build advanced AI-generated content (AIGC) quality frameworks and agentic flows to generate content for proactive surfaces like Discover and Notifications.
Develop AI-generated content through advanced context engineering and agentic feedback loops to identify and deliver engaging stories globally.
Conduct advanced quality evaluations and leverage downstream dense recommendation signals and feedback to improve the performance of the AIGC stack.
Resolve key system-level bottlenecks in recommending and serving AIGC content to optimize efficiency for low-latency surfaces.
Navigate high ambiguity, drive technical innovation for AIGC quality, and influence the broader organizational machine learning strategy.
Qualifications
Minimum
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience in machine learning, recommendation systems, natural language processing, or a related field.
Experience building offline and online quality evaluation frameworks for Large Language Models (e.g., LLM-as-a-judge, RLHF, DPO).
Experience integrating generative AI tools or LLM interfaces into workflows.
Preferred
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
8 years of experience with data structures/algorithms.
3 years of experience in a technical leadership role leading project teams and setting technical direction.
Experience optimizing machine learning inference, resolving system-level bottlenecks, and improving serving efficiency for low-latency global surfaces.
Experience integrating large language model quality evaluation frameworks with downstream recommendation signals.