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

About the role

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.

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.

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

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• 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.