Machine Learning Engineer

About the role

MINIMUM QUALIFICATIONS:

• 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

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• 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.