Lead, Large-Scale ML & Recommender Systems

Google LLC; San Bruno (CA); On-site; USD 262,000 - 364,000; Full time

Benefits offered by this job

Equity

Bonus target

Comprehensive benefits

Job summary

Google LLC in Mountain View, CA is seeking a Software Engineering Manager to lead teams across information retrieval, AI, NLP, and large-scale system design. You will guide ML-driven recommender projects, align cross-functional roadmaps, and mentor engineers to optimize both code and processes.

Responsibilities include setting team priorities, guiding technical strategy, and overseeing deployment of large-scale ML models across multiple sites.

Qualifications

• 8 years designing and building large-scale ML models.
• 8 years launching software products with experimentation and user data.
• 5 years leading teams and managing people.
• Experience aligning cross-organizational roadmaps across multiple teams.
• Experience with recommender systems research or applied research.

Responsibilities

• Set and communicate team priorities that support broader goals and align across squads.
• Coach engineers, define performance expectations, and drive development discussions.
• Develop long-term ML goals and roadmaps beyond current teams and adapt to future needs.
• Oversee system designs and review code to solve complex problems.
• Lead ML infrastructure optimization and advanced recommendation system work.

Skills

Leadership in ML systems

Recommender systems

Cross-functional collaboration

Team management

Large-scale ML models

Education

Bachelor's degree in Computer Science or related field

Master's degree or PhD preferred

Tools

Gemini-based LLMs

Semantic IDs

ML infrastructure

Job description

Google LLC in Mountain View, CA is seeking a Software Engineering Manager to lead teams across information retrieval, AI, NLP, and large-scale system design. You will guide ML-driven recommender projects, align cross-functional roadmaps, and mentor engineers to optimize both code and processes.

Responsibilities include setting team priorities, guiding technical strategy, and overseeing deployment of large-scale ML models across multiple sites.