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. Google’s machine learning models drive the global Ads business, serving billions of users and generating significant business. As a Software Engineer on the Proxybidder ML team, you will be involved in the full machine learning model lifecycle - from design and training to deployment and serving in production at the scale of billions of Search Ads. We are the team specializing in AI and ML for Search Ads bidding. Our mission is to build and deploy the large-scale Machine Learning models that predict user value, empowering advertisers to achieve peak return on investment (ROI) and driving a vital component of Google's business. 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 . • Develop and maintain machine learning models using advanced AI techniques to predict user interactions and optimize advertiser return on investment (ROI). • Innovate on machine learning model design to improve quality, stability, and efficiency throughout the entire model lifecycle. • Analyze experiments using statistical methods to solve complex machine learning problems and improve model generalization. • Enhance model health and stability by contributing to code health, automation, and alerting systems. • Collaborate with Research and Infrastructure teams to test and implement the latest technologies in production environments. • Bachelor’s degree or equivalent practical experience. • 2 years of experience with software development in Python or C++ . • 2 years of experience applying mathematical modeling, numerical analysis, or statistical methods to solve engineering or scientific problems. • 1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production. • 1 year of experience with end to end machine learning (e.g., model deployment, model evaluation, optimization, data processing, debugging). Minimum qualifications: • Bachelor’s degree or equivalent practical experience. • 2 years of experience with software development in Python or C++ . • 2 years of experience applying mathematical modeling, numerical analysis, or statistical methods to solve engineering or scientific problems. • 1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production. • 1 year of experience with end to end machine learning (e.g., model deployment, model evaluation, optimization, data processing, debugging). Preferred qualifications: • Master's degree or PhD in Computer Science or related technical fields. • Experience with machine learning, statistical analysis, applied math, or operation research in the industry or in the academic sector. • Experience productionizing machine learning systems or designing experiments. • Experience with Google Ads systems or specialized ML tools such as TensorFlow, Keras, or TFX. • Ability to to write high quality and low latency code/models that can train on and serve on every query.

Requirements

Must-haves

Google’s machine learning models drive the global Ads business, serving billions of users and generating significant business.
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 .
Develop and maintain machine learning models using advanced AI techniques to predict user interactions and optimize advertiser return on investment (ROI).
Innovate on machine learning model design to improve quality, stability, and efficiency throughout the entire model lifecycle.
Analyze experiments using statistical methods to solve complex machine learning problems and improve model generalization.
Enhance model health and stability by contributing to code health, automation, and alerting systems.
Collaborate with Research and Infrastructure teams to test and implement the latest technologies in production environments.
Bachelor’s degree or equivalent practical experience.
2 years of experience with software development in Python or C++ .
2 years of experience applying mathematical modeling, numerical analysis, or statistical methods to solve engineering or scientific problems.
1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
1 year of experience with end to end machine learning (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Bachelor’s degree or equivalent practical experience.
2 years of experience with software development in Python or C++ .
2 years of experience applying mathematical modeling, numerical analysis, or statistical methods to solve engineering or scientific problems.
1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.

Nice-to-haves

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Master's degree or PhD in Computer Science or related technical fields.
Experience with machine learning, statistical analysis, applied math, or operation research in the industry or in the academic sector.
Experience productionizing machine learning systems or designing experiments.
Experience with Google Ads systems or specialized ML tools such as TensorFlow, Keras, or TFX.
Ability to to write high quality and low latency code/models that can train on and serve on every query.

Application prep

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

Keywords to mirror

machine learningmlaipython

Platform: google
Effort: medium