Staff ML Engineer, Search AI Generated Content Quality
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Apply to Staff ML Engineer, Search AI Generated Content Quality at GoogleJob details
- Location
- Mountain View, California
- Work type
- Onsite
- Compensation
- $207,000 - $300,000/yr
- Posted
- yesterday
- Apply on
- careers.google.com
About this role
Minimum qualifications:
- 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 qualifications:
- 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.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
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.
In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
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.