Senior Machine Learning Engineer, Shopping AI
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
Apply Now
Apply Now
• 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
Apply Now
Apply Now