Distinguished Scientist

Job not on LinkedIn

🕒 5 days ago

🏄 California, Colorado, +16 more states – Remote

🏄 California – Remote ⛰️ Colorado – Remote 🦌 Connecticut – Remote 🏛️ District of Columbia – Remote 🌺 Hawaii – Remote 🌽 Illinois – Remote 🦞 Maine – Remote 🎰 Nevada – Remote 🏖️ New Jersey – Remote 🗽 New York – Remote 🏈 Ohio – Remote 🦀 Maryland – Remote 🍂 Massachusetts – Remote ❄️ Minnesota – Remote ⚓ Rhode Island – Remote 🍁 Vermont – Remote ⚔️ Virginia – Remote ☕ Washington – Remote

💵 $270.7k - $432.3k / year

⏰ Full Time

🟠 Senior

🔴 Lead

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Zillow

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2006

🏠 Real Estate

🛍️ eCommerce

👥 B2C

💰 $4.1M Post-IPO Equity on 2012-12

Real Estate • eCommerce • B2C

Zillow is a leading real estate and property rental marketplace that provides comprehensive information on homes, apartments, and properties for sale or rent. It offers users tools to search for properties, calculate mortgage rates, and connect with real estate agents. The platform also features innovative algorithms that provide Zestimates, which are estimated market values of homes. Zillow is a go-to resource for individuals looking to buy, sell, or rent properties, as well as for agents and brokers who want to reach a wider audience.

📋 Description

• Define and drive the technical vision for long-horizon, supervised agentic systems at Zillow. • Design agent architectures, memory and supervision frameworks, and learning strategies that allow our agents to build and maintain context-rich relationships over weeks and months, not just single sessions. • Own the end-to-end research and technical strategy for long-horizon agentic experiences across shopping, financing, and professional workflows. • Design and advance LLM post-training and evaluation methods tailored to supervised, high-stakes journeys in a complex, regulated domain. • Architect systems that combine persistent memory, tool use, and multi-agent collaboration to deliver consistent, context-rich guidance. • Collaborate with product and design to define success metrics, evaluation frameworks, and experiment plans for agentic experiences. • Represent Zillow’s work externally through publications, talks, open-source contributions, and thoughtful engagement with the research community.

🎯 Requirements

• A PhD in Computer Science, Electrical Engineering, or a related field—or equivalent experience—with emphasis in areas such as foundational LLMs, agentic AI, reinforcement learning, AI planning, or natural language processing • 10+ years of hands-on experience building and deploying large-scale AI systems, including at least several years focused on agent-based systems, multi-agent collaboration, or long-horizon conversational assistants • Deep, current expertise in generative and agentic AI, including multimodal foundation models, transformers, advanced reasoning models, and post-training techniques (SFT, DPO, RLHF/RLAIF, preference learning, etc.) • A track record of leading ambiguous, cross-functional initiatives from concept to production—framing the problem, shaping data strategy, designing models and evaluation, and iterating based on live metrics and user feedback. • Demonstrated impact in the research community through publications at top venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR) and/or widely used open-source contributions in LLMs, agentic frameworks, or related areas. • Experience designing evaluation and supervision frameworks for long-horizon agents, ideally in regulated or high-stakes domains such as finance, healthcare, or large-scale marketplaces, with an emphasis on safety, fairness, and trust. • Strong technical leadership skills: you mentor senior scientists and engineers, create clarity amid ambiguity, and build alignment across research, engineering, product, and leadership stakeholders. • Excellent communication skills with the ability to distill complex ideas into clear narratives for executives, cross-functional partners, and external audiences.

🏖️ Benefits

• Competitive base salary • Equity awards based on factors such as experience, performance and location