Principal Applied Scientist, Agentic AI

About the role

Zillow is investing deeply in next‑generation AI and machine learning to power intelligent experiences across our products, helping customers and partners make better decisions in a complex, real‑world domain. Our team brings together Applied Scientists, ML engineers, and Software engineers who own the full lifecycle of large‑scale systems that combine modern foundation models with applied ML-from data and modeling through evaluation and deployment. We collaborate closely with platform, product, and operations partners in a fast‑moving, remote‑first environment where experimentation, learning, and shipping are core to how we work. As a Principal Applied Scientist focused on RL post‑training, you will lead the design and deployment of learning systems that shape how our models behave in real products. You will own the technical direction and strategy for post‑training and adaptation of large models to align behavior with user value, safety, and business objectives. This is a high‑impact principal IC role with broad influence across Zillow, working closely with senior leadership to ensure our investments translate into safer, more capable, and more trusted AI‑powered experiences. You will get to: Lead the technical direction and strategy for RL post‑training of production models, partnering with other scientists, engineers, and product leaders to align models with customer and business needs. Design and implement post‑training pipelines that combine techniques such as supervised fine‑tuning on curated demonstrations, preference modeling and pairwise ranking, and RL‑based alignment approaches like RLHF, RLAIF, or DPO for multi‑objective optimization. Develop reward models and objective formulations that balance constraints such as helpfulness, safety, fairness, compliance, and customer satisfaction, and iterate on them using human and AI feedback at scale through online and batch adaptation loops with strong guardrails. Translate conversational logs, behavioral signals, and structured attributes into training, reward, and evaluation signals for post‑training and reinforcement learning, turning heterogeneous data into actionable supervision. Partner with model and platform teams to improve the efficiency and robustness of training and evaluation, including off‑policy evaluation, replay strategies, controlled rollouts, and metrics and evaluation frameworks such as win‑rates versus baselines, safety and quality metrics, and expert‑review programs. Mentor applied scientists and engineers, raising the technical bar in RL, post‑training, and evaluation, and contributing to the broader AI roadmap at Zillow through thought leadership and guidance. When appropriate, represent Zillow's work externally through talks, publications, or open‑source contributions. 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 exceptions. In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $191,300.00 - $305,700.00 annually. This base pay range is specific to these locations and may not be applicable to other locations. In Colorado, Hawaii, Illinois, Minnesota, Nevada, Ohio, Rhode Island, and Vermont the standard base pay range for this role is $181,800.00 - $290,400.00 annually. The base pay range is specific to these locations and may not be applicable to other locations. In 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.