Software Engineer, AI/ML, Shopping Relevance Models

Software Engineer, AI/ML, Shopping Relevance Models

Share Software Engineer, AI/ML, Shopping Relevance Models

corporate_fare Google place Mountain View, CA, USA

• Bachelor's degree or equivalent practical experience.
• 2 years of experience programming in Python or C++.
• 1 year of experience with end to end machine learning (e.g., model deployment, model evaluation, optimization, data processing, debugging).
• 1 year of experience with one or more of the following: reinforcement learning (e.g., sequential decision making), ML infrastructure, Ranking, or Recommendations.

Preferred qualifications:

• Master's degree or PhD in Computer Science, or a related technical field.
• 2 years of experience with data structures or algorithms.
• Experience in statistics, databases, analytics, big data, or a related area.

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 mission of the Shopping Ads Relevance models team is to improve the matches between users' queries and tasks, and the product listing ads that we show. We use state of the art machine learning techniques to predict human ratings of ads and incorporate those into filtering and ranking of ads. Our work on relevance contributes to product excellence for the shopping ads.

Our team builds and deploys ML models to predict the relevance of shopping ads shown on Google.com search results, Google image search results, and in the Google shopping mode. These models are used both in the retrieval phase where we first match products to users’ queries and tasks, and in the ads auction where we determine which ads are eligible to show, rank and price.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

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 mission of the Shopping Ads Relevance models team is to improve the matches between users' queries and tasks, and the product listing ads that we show. We use state of the art machine learning techniques to predict human ratings of ads and incorporate those into filtering and ranking of ads. Our work on relevance contributes to product excellence for the shopping ads.

Our team builds and deploys ML models to predict the relevance of shopping ads shown on Google.com search results, Google image search results, and in the Google shopping mode. These models are used both in the retrieval phase where we first match products to users’ queries and tasks, and in the ads auction where we determine which ads are eligible to show, rank and price.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google .

• Write product or system development code.
• Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
• Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
• Train machine learning models and explore model features, architectures, and hyperparameters in order to continuously improve model accuracy.

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, are subject to approval by the Alphabet Inc. board of directors or its delegate, 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.