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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.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- 3 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, and embedding) in production, and experience building architecture.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- 1 year of experience with machine learning frameworks such as TensorFlow, PyTorch, or JAX.
Preferred qualifications: - Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience with data structures and algorithms.
- Experience developing accessible technologies.
- Understanding of modern recommendation architectures (e.g., two-tower models, sequential user modeling) and how to integrate large foundation models into existing ranking/retrieval stacks.
About the jobIndividual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Write and test 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.
- Design and implement recommendation systems models across different domains, leverage ML infrastructure, and contribute to architecture design.
