Machine Learning Engineer, Zillow Shopping AI

Zillow Zillow · Consumer · United States · Remote

Machine Learning Engineer at Zillow Shopping AI to build and own production ML systems for core user experiences like ranking, recommendations, search, and autocomplete. The role involves designing, building, and shipping new ML models, re-architecting ranking systems, owning the full model lifecycle, and pioneering generative AI/LLM applications. Collaboration with cross-functional teams and contribution to ML infrastructure are also key.

What you'd actually do

  1. Design, build, and ship production new machine learning models that power core product features on the Zillow app, website, and email/push notifications.
  2. Help re-architect our core home ranking and recommendation systems to support advanced neural networks and dramatically accelerate the pace of experimentation across surfaces.
  3. Own the full lifecycle of your models, from offline experimentation and prototyping with massive datasets to online deployment, A/B testing, and performance monitoring.
  4. Pioneer the application of cutting-edge deep learning and large language models (LLMs) to improve our home shopping experience.
  5. 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.

Skills

Required

  • developing applications in search, personalized ranking, or recommender systems
  • developing and deploying ML models that scale to high-traffic, latency sensitive customer-facing services
  • Strong programming skills in a high-level language such as Python or Java
  • common machine learning libraries like PyTorch, TensorFlow, Catboost, scikit-learn and huggingface (repository)
  • large scale distributed data processing systems such as Hive, Spark, Airflow, or Databricks
  • owning the full lifecycle of customer facing machine learning models, from offline experimentation and prototyping to online deployment, A/B testing, and performance monitoring

Nice to have

  • Master's degree + 2 yrs or BS with a minimum of 4 yrs of experience
  • Prior experience or high level of curiosity with generative AI and excitement to collaborate on what they’ve learned!

What the JD emphasized

  • scale to high-traffic, latency sensitive customer-facing services (100s of millions of requests per day)
  • owning the full lifecycle of customer facing machine learning models, from offline experimentation and prototyping to online deployment, A/B testing, and performance monitoring

Other signals

  • production machine learning systems
  • personalized ranking & recommendations
  • semantic search
  • autocomplete
  • display optimization
  • generative AI
  • large language models (LLMs)