Staff Software Engineer, Machine Learning, Discover Ads Retrieval

Staff Software Engineer, Machine Learning, Discover Ads Retrieval corporate_fare Google place Mountain View, CA, USA bar_chart Advanced Advanced Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.

Minimum qualifications:

Bachelor's degree or equivalent practical experience. 8 years of experience with one or more general purpose programming languages including but not limited to: Java, C/C++ or Python. 8 years of experience in software development. 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains. 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

Preferred qualifications:

8 years of experience with data structures and algorithms. 6 years of experience with ML or quality, working on recommendation systems. Experience in recommender systems, clustering algorithms, SQL, and deep model. Experience in C++, Dremel/F1, and TensorFlow. Research experience. Ability to drive quality projects end-to-end from design to implementation to eventual launch. About the job The Discover Ads Retrieval team is looking for a Tech Lead to advocate the retrieval strategy that connects billions of users with the most relevant ads in their Discover Feed. Discover Feed has ~2B monthly active users and is a primary driver of Google's "Social Ads" effort. Unlike traditional Search, Discover is a proactive, recommendation-driven surface. This creates a unique modeling challenge: how do we retrieve high-quality, relevant ads that provide real value to a user before they even ask for them? The Retrieval team is responsible for the "Top of the Funnel". We innovate on the ML models and applicant generation strategies that filter millions of potential ads down to a high-quality subset. Our work directly determines the upper bound of the entire ads system's performance, impacting every Discover user globally. Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We're made up of multiple teams, building Google's Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale. The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about . Responsibilities Be the primary technical authority for our retrieval stack and move beyond incremental tuning to drive step-functions in ad relevance and quality by leading a team of ML engineers to explore the frontiers of embedding-based retrieval, deep learning architectures, and multi-objective optimization. Define roadmap for retrieval quality. This includes moving from legacy retrieval methods (e.g., two-tower models, transformer-based embeddings, and generative retrieval). Build close partnership with many relevant modeling teams. Innovate on how we measure and improve "relevance". Lead efforts to align retrieval outputs with long-term user satisfaction and advertiser Return on Investment (ROI), ensuring we aren't just retrieving "clicks," but "value." Design next-generation applicant generation strategies. Oversee the integration of fresh signals (user intent, content semantics, and social trends) into our retrieval models to capture the dynamic nature of the Discover Feed.