Machine Learning Engineer, Zillow Shopping AI

Machine Learning Engineer, Zillow Shopping AI page is loaded## Machine Learning Engineer, Zillow Shopping AIremote type:
Remote locations:
Remote-USAtime type:
Full time posted on:
Posted Todayjob requisition :
P747947## About the team

As the engine behind Zillow Group's 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. Our team of engineers and scientists builds and owns the production machine learning systems that power Zillow's core user experience, including personalized ranking & recommendations, semantic search, autocomplete, and display optimization.

Working closely with product and design, we apply the latest advancements in AI to solve unique, large-scale challenges. As we look forward, we are tackling the next generation of questions, including how generative AI can unlock intuitive, personalized experiences for every home shopper.## About the role
** You Will Get To:
*** 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 team's strategic roadmap.
* Contribute to the team's engineering excellence by improving our machine learning infrastructure, development standards, and shared tooling.

This role has been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited  California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $ - $ annually.

This base pay range is specific to these locations and may not be applicable to other  Colorado, Hawaii, Illinois, Minnesota, Nevada, Ohio, Rhode Island, and Vermont the standard base pay range for this role is $ - $ annually. The base pay range is specific to these locations and may not be applicable to other  addition to a competitive base salary this position is also eligible for equity awards based on factors such as experience, performance and location.

Actual amounts will vary depending on experience, performance and location. Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside.## Who you are
** Must Have:
*** 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)
* Strong programming skills in a high-level language such as Python or Java
* Familiarity with common machine learning libraries like PyTorch, Tensor Flow, 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
* Here at Zillow - we value the experience and perspective of candidates with non-traditional backgrounds. We encourage you to…