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Google

Senior Software Engineer, Machine Learning, Discover Interest Modeling and Retrieval

Mountain View, CA, United States

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Job details

Experience: 5 years of experience building and launching production ML models, including 2 years of experience with recommendation, retrieval, search, or personalization systems.
Posting language: English

Original job description

In most instances, this position requires in-person interviews as part of the hiring process.

Minimum qualifications:

Bachelor’s degree or equivalent practical experience.
5 years of experience building and launching production ML models, including 2 years of experience with recommendation, retrieval, search, or personalization systems.
1 year of experience in the design, analysis, and optimization of algorithms for high-performance systems, and developing and implementing Machine Learning algorithms (e.g., classification, regression, clustering, deep learning) for practical applications.
Experience with large-scale data processing (Beam, Spark, or similar), in Python, C++, and SQL, and with JAX, TensorFlow, or PyTorch.

Preferred qualifications:

Master's degree or PhD in Computer Science or related technical field.
5 years of experience with data structures and algorithms.
1 year of experience in a technical leadership role.
Experience developing accessible technologies.
A proven track record driving quality projects end to end, from design through A/B experimentation to launch.

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.

Discover is Google's personalized interest feed. With hundreds of millions of daily active users, it is one of the largest feed recommendation products in the world.

In this role, the profile captures each person's long-term interests, favorite creators and in-the-moment intent. As these models drive hundreds of billions of recommendations a day, among the most important personalization signals in downstream ranking, and feed personalization across Google.

Every Discover feed starts with us, from a constantly refreshing corpus of millions of articles, videos, creator posts and AI-generated content, our retrieval and pre-ranking stack selects the few thousand candidates most relevant to each person, in milliseconds, at hundreds of millions of feeds a day.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: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Write and test product or system development code.
Develop representation learning models, including embedding or encoder models trained with contrastive, supervised, or self-supervised (e.g., masked-prediction) objectives, and set or sequence encoders (attention pooling, Transformers).
Execute clustering or metric learning at scale (e.g., hierarchical/agglomerative, density-based), and assess cluster quality.
Utilize Large Language Model (LLMs) in ML systems (e.g., LLM embeddings, autoraters for evaluation) and distill LLM judgments into classifiers.

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Skills

Machine learning
ML
Deep learning
Process data
Apache Spark
Python
C++
SQL
TensorFlow
PyTorch
Computer science
Distributed computing
Data storage
Natural language processing
UI design
Leadership
Encoder

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