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X In most instances, this position requires in-person interviews as part of the hiring process.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in C , Java, and using LLM coding tools.
- 1 year of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
- 1 year of experience with one or more of the following: Gen AI/LLM tools, reinforcement learning (e.g., sequential decision making), Machine Learning (ML) infrastructure, or specialization in another ML field.
Preferred qualifications: - Master's degree or PhD in Computer Science, or in a related technical field.
- Experience with the latest research and trends in AI/ML.
- Experience designing and building scalable fullstack systems and self-service platforms.
- Knowledge of search/Ads stack.
- Excellent analytical thinking and data analysis skills.
About the jobGoogle 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 .
Responsibilities - Write product or system development code.
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- 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.
