Machine Learning Staff Software Engineer, Search Personalization

Socket.dev; Mountain View (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google is hiring software engineers to develop next-generation personalization technologies. You will work on models for neural retrieval, clustering, and ranking to power Discover in Search. Roles involve handling scale, elasticity, and integration across product surfaces.

The team focuses on user modeling, retrieval, and generation with opportunities to impact billions of users across Google products. Collaboration and leadership are valued.

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 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 and ranking models.
• Build user and content clustering models to enable core personalization and ranking use cases.
• Enhance model performance and personalization through advanced modeling techniques and feature engineering.
• Scale model applications to multiple modalities and use cases (retrieval, ranking, content generation, diversification).
• Create next-generation realtime ML models and scale training and serving to billions of users.

Skills

Software development

Recommendation systems

Model architecture

ML design

ML infrastructure

Software testing

Product launch

Software design & architecture

End-to-end delivery

Data structures & algorithms

ML for recommender systems

Clustering algorithms

SQL

C++

TensorFlow

Research experience

Education

Bachelor’s degree or equivalent practical experience

Tools

C++

Dremel/F1

TensorFlow

Job description

MINIMUM 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.

PREFERRED QUALIFICATIONS:

• 8 years of experience with data structures and algorithms.
• 6 years of ML or Quality experience working on recommendation systems.
• Experience in recommender systems, clustering algorithms, SQL, deep model.
• Experience in C++, Dremel/F1 and TensorFlow.
• Experience working with research.
• Ability to drive quality projects end-to-end from design to implementation to eventual launch.

ABOUT THE JOB:

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

As a part of the Discover Personalization team, you will help people feel positively connected and informed about the world around them by delivering the pulse of the Internet that matters to you, within Google Search. You will contribute to the key product's appeal, which lies in having an understanding of users and will be laser focused on building foundational user models for users using all of their interactions across Google products, and leveraging them to power Discover’s retrieval and ranking. You will manage some of the toughest ML and Quality problems, including Neural Deep Retrieval, Activity Clustering, Reinforcement Learning and Multi-Objective Ranking.

In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Googlehttps://www.google.com/about/careers/applications/benefits/.

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.