Machine Learning Staff Software Engineer, Search Personalization

About the role

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 Google

[https://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.