[Hiring] Senior Applied Scientist @Zillow

Role Description

The Agentic Foundations team is a focused group of 15–20 applied scientists within Zillow's broader Agentic AI organization, building the foundational AI technologies that power the next generation of home shopping experiences. This Senior Applied Scientist will focus on model post-training and reward modeling — the science that lets our agents measurably improve over time as part of a self-evolving agentic system. The role will drive complex, ambiguous work end-to-end, partnering closely with product, engineering, science, platform, and data teams to translate the latest advances in LLMs, reinforcement learning, and post-training into production impact.

This is an exciting time to join as Zillow is building agentic skills across the full home-shopping journey — from search and personalized guidance to offer strategy and financing — at a scale of hundreds of millions of requests per day.

Qualifications

• Master's degree or higher in Computer Science or a related field.
• Hands-on experience with LLM post-training and RL fine-tuning: SFT, DPO, RFT/GRPO, running end-to-end training runs on GPU infrastructure.
• Exposure to reward model development — research-level experience is sufficient; production deployment is not required.
• Strong knowledge of generative AI, including foundation models, transformers, reinforcement learning, and preference learning.
• Ability to independently scope and solve ambiguous problems end-to-end while providing technical leadership to scientists and MLEs.
• Strong programming skills, especially Python, plus experience with ML frameworks such as PyTorch or TensorFlow.

Requirements

• Own LLM post-training pipelines — SFT, DPO, and reinforcement fine-tuning (RFT/GRPO) — running training end-to-end on GPU infrastructure.
• Build and train fast, multi-category reward models (PRMs) that emit scalar signals from contrastive preference pairs.
• Design on-policy and online assessment: score live agent trajectories using LLM-as-a-Judge frameworks.
• Translate offline evaluation rubrics into generalizable reward functions that work on arbitrary production traces.
• Independently drive complex work end-to-end by collaborating across partner teams.
• Provide technical leadership and mentorship while helping translate state-of-the-art AI research into production impact.

Benefits

• In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $160,900.00 - $257,100.00 annually.
• In Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia the standard base pay range for this role is $152,900.00 - $244,300.00 annually.
• In addition to a competitive base salary, this position is also eligible for equity awards based on factors such as 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.

Company Description

At Zillow, we’re reimagining how people move—through the real estate market and through their careers. As the most-visited real estate platform in the U.S., we help people navigate buying, selling, financing, and renting with greater ease and confidence.

How we work is a key part of that transformation. Through Cloud HQ, our strategic approach to distributed work, most employees have the flexibility to work from wherever they’re most productive—enabling us to move fast, stay connected, and deliver on our people promise.

Zillow is honored to be recognized among the best workplaces in the U.S. Zillow was named one of FORTUNE 100 Best Companies to Work For® in 2026, and included on TIME’s America’s Best Companies 2026 list, reflecting our commitment to creating an innovative, inclusive, and engaging culture where employees are empowered to grow.

No matter where you sit in the organization, your work will help drive innovation, support our customers, and move the industry—and your career—forward, together.