Software Engineer, AI-Driven Shopping & Recommendations

Google LLC; Mountain View (CA); On-site; USD 147,000 - 210,000; Full time

Job summary

Google LLC in Mountain View, CA is seeking a Software Engineer to work on Consumer Shopping Quality within Commerce, Recommendations, Rankings, and Predictions. You will write production code, collaborate with peers and stakeholders through design reviews, and help shape scalable AI-powered shopping experiences across Google's products.

This role focuses on building and deploying recommendation models, optimizing ML infrastructure, and driving improvements in retrieval, ranking, and

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with C++ and Python programming languages, or 1 year of experience with an advanced degree.
• 1 year of experience building and deploying recommendation systems models in production.
• 1 year of experience with ML infrastructure (e.g., model deployment, evaluation, optimization, data processing).

Responsibilities

• Write product or system development code.
• Collaborate with peers and stakeholders 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/resolve impacting hardware, network, or service operations.
• Build and deploy recommendation systems models and contribute to model optimization and data processing.

Skills

C++

Python

Education

Bachelor’s degree or equivalent practical experience

Master’s degree or PhD in CS or related field

Job description

Google LLC in Mountain View, CA is seeking a Software Engineer to work on Consumer Shopping Quality within Commerce, Recommendations, Rankings, and Predictions. You will write production code, collaborate with peers and stakeholders through design reviews, and help shape scalable AI-powered shopping experiences across Google's products.

This role focuses on building and deploying recommendation models, optimizing ML infrastructure, and driving improvements in retrieval, ranking, and