Senior Software Engineer, AI/ML, Search Personalization

Google•Mountain View, CA

2d•$174,000 - $252,000

About The Position

Google software engineers develop technologies used by billions of people to connect, explore, and interact with information. The role involves work on a project critical to Google, with opportunities to move between teams and projects as the business evolves. Engineers are expected to be versatile, show leadership qualities, and take on problems across the full stack. Within Google Search, this position contributes to efforts to reimagine how people search for information, address complex engineering challenges, and expand infrastructure while maintaining an accessible and useful experience. The engineer will design and implement recommendation system models across domains, use machine learning infrastructure, and contribute to architecture design. Candidates should be prepared to discuss their knowledge and use of AI tools during the hiring process. Individual pay is determined by job-related skills, experience, and relevant education or training. The listed US compensation is $174,000–$252,000, plus a 15% bonus target, equity, and benefits.

Requirements

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 system models in production, including retrieval, prediction, ranking, personalization, search quality, and embedding, and experience building architecture.
3 years of experience with ML infrastructure, such as model deployment, model evaluation, optimization, data processing, or debugging.
1 year of experience with machine learning frameworks such as TensorFlow, PyTorch, or JAX.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
5 years of experience with data structures and algorithms.
Experience developing accessible technologies.
Understanding of modern recommendation architectures, such as two-tower models and sequential user modeling, and integrating large foundation models into existing ranking and retrieval stacks.

Responsibilities

Write and test product or system development code.
Collaborate with peers and stakeholders through design and code reviews to ensure best practices in areas such as style guidelines, code submission, accuracy, testability, and efficiency.
Contribute to existing documentation or educational content and adapt it based on product or program updates and user feedback.
Design and implement recommendation system models across different domains, leverage ML infrastructure, and contribute to architecture design.
Utilize AI tools and technologies and discuss knowledge and use of AI tools during the hiring process.