Principal Scientist, Agentic Systems Applied Science

About the role

The Principal Scientist will establish how agentic AI systems are built, evaluated, and scaled at Wayfair. This role sits within the GST + SCRT Applied Research organization and focuses on one of the most consequential open problems in applied AI: how to build autonomous systems that can reason, plan, and act across complex supplier and supply chain workflows—reliably, safely, and at scale.

Responsibilities

• Research and prototype agentic architectures — including planning, memory, tool use, and multi-step reasoning — relevant to catalog and supply chain workflows.
• Define Wayfair's evaluation frameworks for agentic systems, moving beyond offline accuracy and A/B tests to assess reliability, safety, and business impact in non-deterministic environments.
• Establish company-wide standards for autonomy boundaries, failure modes, rollback procedures, and human-in-the-loop design — and drive adoption across technology teams.
• Build reusable agentic patterns that Applied Science and Engineering teams can adopt safely, reducing organizational debt and preventing fragmented, one-off implementations.
• Identify where agentic approaches outperform deterministic pipelines — and where they do not — serving as a system-level gate on investment decisions.
• Partner with downstream teams in Supply Chain, Search, and Customer Technology to pilot validated patterns and demonstrate measurable business impact.
• Influence platform strategy by providing input on tooling, evaluation infrastructure, and resource allocation decisions for agent development across the organization.
• Define a proactive research agenda that keeps Wayfair at the frontier of agentic AI capabilities as the field evolves.

Requirements

• Deep hands-on expertise in LLM-based systems, agent architectures, and multi-step reasoning pipelines — you have built these in production, not just studied them.
• Experience designing evaluation frameworks for complex, non-deterministic AI systems, with a strong understanding of where standard metrics fall short.
• A track record of technical influence across engineering, applied science, and product organizations without requiring direct organizational authority.
• The judgment to identify when agentic approaches should not be used — and the credibility to make that call stick with senior stakeholders.
• Comfort operating in ambiguous, greenfield problem spaces where the right framework does not yet exist.
• PhD or equivalent research experience in machine learning, AI, or a related field, combined with meaningful industry experience building systems at scale.
• Strong communicator with a proven ability to translate complex research findings for technical and non-technical audiences alike.

Qualifications

• PhD or equivalent research experience in machine learning, AI, or a related field, combined with meaningful industry experience building systems at scale.
• Experience designing evaluation frameworks for complex, non-deterministic AI systems, with a strong understanding of where standard metrics fall short.
• Deep hands-on expertise in LLM-based systems, agent architectures, and multi-step reasoning pipelines — you have built these in production, not just studied them.
• The judgment to identify when agentic approaches should not be used — and the credibility to make that call stick with senior stakeholders.
• Comfort operating in ambiguous, greenfield problem spaces where the right framework does not yet exist.
• A track record of technical influence across engineering, applied science, and product organizations without requiring direct organizational authority.
• Strong communicator with a proven ability to translate complex research findings for technical and non-technical audiences alike.

Skills

• Expertise in LLM-based systems, agent architectures, and multi-step reasoning pipelines.
• Experience with evaluating complex, non-deterministic AI systems.
• Ability to influence technical and non-technical stakeholders.
• Comfort with ambiguity and greenfield problem spaces.
• Strong communication skills.

Benefits

Annual Base Pay: USD 228,000.00 - USD 234,000.00

Pay

Annual Base Pay: USD 228,000.00 - USD 234,000.00

Schedule

Full-time