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    Home/Jobs/Machine Learning Engineer, Zillow Shopping AI

    Machine Learning Engineer, Zillow Shopping AI

    Zillow

    Richmond
    1-3 years
    Today
    ₹138–232 LPA
    Full-time
    Remote

    Skills Required

    Hugging Face
    LLM
    Python
    PyTorch
    TensorFlow
    Catboost
    Scikit-learn
    Hive
    Apache Spark
    Apache Airflow
    Databricks
    Machine Learning
    Deep Learning

    Description

    As the engine behind Zillow Groups mission to build a seamless digital real estate marketplace, the Shopping AI team is fundamentally redefining how millions of people discover and shop for homes.

    Company: Zillow

    Role: Machine Learning Engineer, Zillow Shopping AI

    Location: Remote

    Experience:

    • 1-3 years of experience in developing applications in search, personalized ranking, or recommender systems
    • Experience developing and deploying ML models that scale to high-traffic, latency sensitive customer-facing services (100s of millions of requests per day)
    • Experience owning the full lifecycle of customer facing machine learning models, from offline experimentation and prototyping to online deployment, A/B testing, and performance monitoring

    Key Skills:

    • Python
    • Java
    • PyTorch
    • TensorFlow
    • Catboost
    • scikit-learn
    • huggingface (repository)
    • Hive
    • Spark
    • Airflow
    • Databricks

    Qualification:

    • Masters degree + 2 yrs or BS with a minimum of 4 yrs of experience (preferably in large consumer tech companies)

    Role Focus:

    • Design, build, and ship production new machine learning models that power core product features on the Zillow app, website, and email/push notifications
    • Help re-architect our core home ranking and recommendation systems to support advanced neural networks and dramatically accelerate the pace of experimentation across surfaces
    • Own the full lifecycle of your models, from offline experimentation and prototyping with massive datasets to online deployment, A/B testing, and performance monitoring
    • Pioneer the application of cutting-edge deep learning and large language models (LLMs) to improve our home shopping experience
    • Develop new AI components that optimize how we display and when we recommend homes, ensuring we connect shoppers with the right content on the right properties at the right time
    • Collaborate in a cross-functional group of engineers, applied scientists, product managers, and designers to define, execute, and iterate on the teams strategic roadmap
    • Contribute to the teams engineering excellence by improving our machine learning infrastructure, development standards, and shared tooling

    Nice to have:

    • Prior experience or high level of curiosity with generative AI and excitement to collaborate on what they’ve learned

    Other:

    • In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $145,500.00 - $232,500.00 annually
    • In Colorado, Hawaii, Illinois, Minnesota, Nevada, Ohio, Rhode Island, and Vermont the standard base pay range is $138,300.00 - $220,900.00
    • Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside
    • This position is also eligible for equity awards based on factors such as experience, performance and location
    • Zillow was named one of FORTUNE 100 Best Companies to Work For in 2025, and included on the PEOPLE Companies That Care 2025 list

    Prepare for this role

    Recommended resources to build the skills for this position. Sponsored.

    Python for Everybody Specialization

    Coursera

    Learn Python from scratch — variables, data structures, web scraping, and databases.

    Python 3 Programming Specialization

    Coursera

    Intermediate Python covering classes, inheritance, APIs, and data processing.

    Generative AI with Large Language Models

    Coursera

    Comprehensive LLM course covering transformer architecture, fine-tuning, RLHF, and deployment.

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