3-5 years experience in search or recommender systems
Deploy ml models to high-traffic latency sensitive services
Strong programming skills in python or java
The Shopping AI team is redefining how millions of people discover and shop for homes by building production machine learning systems for core user experiences
Job Summary
The Shopping AI team is redefining how millions of people discover and shop for homes by building production machine learning systems for core user experiences.
This role involves designing and shipping new ML models that power personalized ranking, semantic search, and the application of cutting-edge deep learning and LLMs.
Zillow Group values non-traditional backgrounds and offers competitive base salary ranges up to $274,300 annually depending on location, along with equity awards.
Matching Summary
The Shopping AI team is redefining how millions of people discover and shop for homes by building production machine learning systems for core user experiences.
Salary
Base: $163,200.00 - $274,300.00 annually depending on state; Equity: Eligible for equity awards based on experience and performance; Benefits: Not specified
Skills & Requirements
Must-have
3-5 years experience in search or recommender systems
Deploy ML models to high-traffic latency sensitive services
Strong programming skills in Python or Java
Expertise with PyTorch, TensorFlow, Catboost, scikit-learn, huggingface
Experience with large scale distributed data processing Hive Spark Airflow Databricks
Own full lifecycle of customer facing ML models from prototyping to monitoring
Nice-to-have
Master's degree plus 3 years or BS with minimum 5 years experience
Prior experience or curiosity with generative AI
Experience at large consumer tech companies
Mentoring other engineers and shaping long-term AI vision
Key Requirements
3-5 years of experience in developing applications in search or recommender systems
BS with minimum 5 years experience or Master's degree plus 3 years
U.S. employees may live in any of the 50 United States with limited exceptions