Senior AI/ML Engineer - Recommendations & Rankings

Google; San Bruno (CA); On-site; USD 121,000 - 202,000; Full time

Job summary

Google’s YouTube team in San Bruno, CA is seeking a Software Engineer III focused on AI/MLRecommendations, Rankings, Predictions. You will prototype and ship features for large-scale user-facing ranking and personalization systems, collaborating across teams to improve search quality and recommendations.

As a mid-level engineer, you’ll mentor junior engineers, drive projects, and contribute to ML deployment and data processing in production.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 2 years of software development experience in one or more programming languages, or 1 year with an advanced degree.
• 2 years of experience with data structures or algorithms.
• 1 year of experience building and deploying recommendation systems models in production.
• 1 year of experience with ML infrastructure (deployment, evaluation, optimization, data processing, debugging).

Responsibilities

• Write product or system development code.
• Collaborate with peers through design and code reviews to ensure best practices.
• Contribute to existing documentation or educational content and adapt content based on product updates and user feedback.
• Triage product or system issues and debug/track/resolve by analyzing sources and impact.
• Build and deploy recommendation systems models and contribute to model optimization and data processing.

Skills

Software development

Data structures & algorithms

Mentoring

Education

Bachelor's degree

Master's degree or PhD (preferred)

Tools

ML infrastructure

Job description

Google’s YouTube team in San Bruno, CA is seeking a Software Engineer III focused on AI/MLRecommendations, Rankings, Predictions. You will prototype and ship features for large-scale user-facing ranking and personalization systems, collaborating across teams to improve search quality and recommendations.

As a mid-level engineer, you’ll mentor junior engineers, drive projects, and contribute to ML deployment and data processing in production.