Staff ML Engineer — Personalization & Search

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 to advance next-generation technologies at massive scale. You will work on projects critical to Google’s needs with opportunities to switch teams as the company grows.

You will tackle information retrieval, distributed computing, large-scale system design, and AI-powered personalization across Google products. As part of the Discover Personalization team, you will build foundational user models from broad user interactions across Google products to power

Qualifications

Bachelor's degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
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 testing, and launching software products, and 3 years of experience with software design and architecture.

Responsibilities

Design and implement personalized user models to optimize for user happiness, including Neural Deep Retrieval Models, Deep Neural Network Ranking/Scoring models, User/Content Clustering Models, Large Language Models (LLM)-based Retrieval Augmented Generation Models, and more.
Build user and content clustering models to enable core personalization and ranking use cases.
Enhance model performance and personalization precision/recall through advanced modeling techniques such as transformers, distillation, reward shaping, multi-task learning, neural bandits, etc. and capabilities through feature engineering, automatic parameter tuning, label quality engineering, etc.
Scale the model's applications to a multitude of modalities (content, queries, videos and notifications) and use cases (retrieval, ranking, content generation, diversification, etc.).
Create next-generation realtime ML models that can capture new user interests and world trends in seconds and scale model training and serving to billions of users.