Lead ML Engineer, Ads Platform & Recommendations

Google

Mountain View (CA)

On-site

USD 207,000 - 300,000

Full time

14 days+

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Job summary

Google is seeking software engineers with deep ML and systems experience to design, deploy, and optimize large-scale software products. The role emphasizes leadership on high-impact projects, collaboration across teams, and advancement of ML infrastructure and data processing strategies.

The position requires strong technical expertise in ML design and architecture, and experience with ranking, retrieval, and embedding models.

Qualifications

  • Bachelor’s degree or equivalent practical experience.
  • 8 years of experience in software development.
  • 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
  • 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).5 years of experience building and deploying recommendation system models (e.g. ranking, retrieval, embedding).

Responsibilities

  • Design, develop, test, deploy, maintain, and enhance large scale software solutions.
  • Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
  • Facilitate alignment and clarity across teams on goals, outcomes, and timelines.
  • Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
  • Influence and coach a distributed team of engineers.

Skills

Software development
ML design
ML infrastructure
Recommendation models

Education

Bachelor’s degree or equivalent
Master’s/PhD in Engineering/CS

Job description

Google is seeking software engineers with deep ML and systems experience to design, deploy, and optimize large-scale software products. The role emphasizes leadership on high-impact projects, collaboration across teams, and advancement of ML infrastructure and data processing strategies.

The position requires strong technical expertise in ML design and architecture, and experience with ranking, retrieval, and embedding models.

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