Job description

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. • 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. Minimum qualifications: • 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. • Experience developing accessible technologies.

Requirements

Must-haves

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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
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.
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.
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.
2 years of experience with data structures or algorithms.

Nice-to-haves

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Master's degree or PhD in Computer Science, or a related technical field.
Experience in statistics, databases, analytics, big data, or a related area.
Experience developing accessible technologies.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlaipython

Platform: google
Effort: medium