Zillow

Compensation

Full Description

Job Description: • Design, build, and ship production new machine learning models that power core product features on the Zillow app, website, and email/push notifications. • 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 team's strategic roadmap. • Contribute to the team's engineering excellence by improving our machine learning infrastructure, development standards, and shared tooling. • Act as a key technical voice, mentoring other engineers and helping to shape the long-term vision for artificial intelligence in the home shopping experience. Requirements: • 3-5 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) • Strong programming skills in a high-level language such as Python or Java • Familiarity with common machine learning libraries like PyTorch, TensorFlow, Catboost, scikit-learn and huggingface (repository) • Expertise with large scale distributed data processing systems such as Hive, Spark, Airflow, or Databricks • 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 Benefits: • equity awards based on factors such as experience, performance and location