Machine Learning Engineer, Search and Shopping Ads

Google Inc.; Northern (KY); Hybrid; USD 262,000 - 364,000; Full time

Benefits offered by this job

25% bonus target

Equity

Benefits

Job summary

Google is seeking a Machine Learning Engineer for Search and Shopping Ads to advance large‑scale ML systems, latency‑aware architectures, and production-grade models. You will collaborate with DeepMind, Research, and Ads ML teams to push the boundaries of AI in search, display, and shopping experiences.

The role emphasizes leadership, mentoring, and delivering end‑to‑end ML solutions that influence ad outcomes and user experience at scale.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of software development with 5 years in large-scale ML/recommenders.
• Experience designing and deploying production DL architectures under latency constraints.
• Experience leading cross-functional technical projects and mentoring engineers.

Responsibilities

• Lead architecture, delivery and cross-team strategy for pCTR models with DeepMind and Ads ML groups.
• Design scalable pCTR architectures maximizing TPU capabilities within low-latency constraints.
• Develop models to capture deep user history for AI Search experiences.
• Engineer loss functions and calibration for business objectives in auctions.
• Build agentic ML workflows to accelerate model architecture discovery.

Skills

ML engineering

Large-scale ML

Cross-functional leadership

ML production

Mentoring engineers

Education

Bachelor's degree or equivalent

PhD preferred

Tools

TensorFlow

TPU

AdBrain

Admixer

Job description

Machine Learning Engineer, Search and Shopping Ads

Advanced

Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders;deep expertise in domain.

In most instances, this position requires in‑person interviews as part of the hiring process. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Pittsburgh, PA, USA.

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience with software development, including 5 years of experience with large‑scale machine learning, deep learning, neural networks, or recommendation systems.
• Experience designing and implementing large‑scale production deep learning or neural network architectures under latency and computational constraints.
• Experience leading cross‑functional technical projects and mentoring other engineers.

Preferred qualifications:

• PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Experience with agent‑driven ML exploration, hyperparameter tuning, or automated model architecture search.
• Deep expertise in one or more of the following: loss engineering for business objectives, joint modeling across distinct prediction stacks, or hardware‑aware ML optimizations (e.g., leveraging dense compute/TPUs effectively).
• Familiarity with ads prediction systems, auction dynamics, or serving infrastructure (e.g., AdBrain, Admixer).
• Demonstrated ability to collaborate with peer technical leads and advanced ML research organizations (such as DeepMind or Google Research) to translate academic or exploratory techniques into production systems.

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.

In this role, you will invent novel, low‑latency architectures that evaluate layouts in milliseconds while maximizing Tensor Processing Unit capabilities. In close collaboration with DeepMind and Research, you will design sequence modeling to capture deep user history across modern experiences like Artificial Intelligence Overviews and Artificial Intelligence Mode. Additionally, you will engineer loss functions for auction dynamics and deploy agentic artificial intelligence workflows to accelerate model discovery. 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. Individual pay is determined by factors including job‑related skills, experience, and relevant education or training.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

• Lead the technical architecture, delivery, and cross‑team strategy for Search and Shopping Ads predicted click‑through rate (pCTR) models in close partnership with DeepMind, Research, and Ads Machine Learning teams.
• Design, prototype, and scale high‑capacity pCTR architectures that maximize modern Tensor Processing Unit (TPU) capabilities while operating within strict low‑latency serving and return‑on‑investment budgets.
• Develop modeling solutions to capture deep user history and nuanced attention signals, seamlessly integrating ads into emerging artificial intelligence Search experiences, including AI Overviews and AI Mode.
• Engineer mathematical loss functions and calibration methods, translating complex business objectives into top‑line metric and auction improvements.
• Build agentic machine learning workflows to automate and accelerate optimal model architecture and feature space discovery.

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents‑to‑be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy , Know your rights: workplace discrimination is illegal , Belonging at Google , and How we hire .

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.

Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.