Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience programming in Python or C .
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- 1 year of experience building or leveraging ML models.
Preferred qualifications:- PhD in a quantitative discipline such as computer science, engineering, mathematics, or physics.
- 2 years of experience with data structures or algorithms.
- Experience in an analytical or quantitative field, like optimization, data science, or machine learning.
- Experience developing accessible technologies.
About the jobThe nature of our work requires a large degree of analytical skills to identify problems and solutions, as well as strong engineering skills to innovate on the infrastructure. Our projects are a mixture of engineering, machine learning and analytical work.
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: $147000 - $211000 (USD) 15% bonus target bonus 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.
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