Senior Machine Learning Scientist - GenAI & Agentic AI

Overview

Wayfair
's mission is to enable everyone to live in a home they love. Achieving this at scale requires sophisticated, trustworthy, and deeply integrated AI systems across the entire business. Within the Science organization, we are building the next generation of agentic AI systems that operate across supply chain, customer outreach, supplier and carrier interactions, catalog management, and merchandising operations.

We are looking for a GenAI & Agentic AI Scientist to define and drive the technical strategy, system design, and evaluation frameworks for agentic AI s is a highly visible, high-impact role for a technical leader who can operate at the intersection of research, engineering, and business decision-making.

• Set Strategy & Technical Direction
  • Partner with leadership to drive the company-wide technical strategy for agentic AI systems, defining architectural standards, design patterns, and best practices that enable scalable, safe, and maintainable AI agents.
  • Act as the lead designer for agentic AI systems spanning supply chain, customer service, supplier and carrier outreach, physical retail, catalog management, merchandising operations, and many other departments.
  • Identify where agentic AI delivers the most value — and push back when alternative ML, automation, or non-AI solutions are more appropriate.
• Lead System Design & Evaluation
  • Design and standardize measurement, validation, and evaluation frameworks for agentic AI systems, including:
  • Task success and business impact metrics
  • Quality, reliability, and failure-mode analysis
  • Human-in-the-loop review and escalation strategies
  • Offline and online evaluation methodologies
  • Establish rigorous approaches to trust, safety, and governance for agentic systems, ensuring outputs are reliable, auditable, and aligned with business constraints.
• Technical Leadership & Execution
  • Tech-lead the evolution of Wayfair’s existing production GenAI products, unifying architectures and implementation patterns to accelerate feature development, extensions, and long-term maintenance.
  • Guide our team and partners on agentic system design, including planning, internal and external systems tool use, agent orchestration, cross-agent communication, and multi-agent coordination.
  • Provide senior-level technical leadership through architecture reviews, code reviews, and design forums across multiple teams.
• Stakeholder Partnership & Influence
  • Work directly with senior business, product, and technology stakeholders to:
  • Translate ambiguous business problems into well-scoped AI system designs
  • Vet technical approaches and tradeoffs
  • Align AI investments with measurable business outcomes
  • Serve as a trusted technical advisor, clearly communicating complex GenAI concepts and influencing decisions at multiple levels of the organization.
• Raise the Bar for GenAI at Wayfair
  • Stay deeply current with advances in LLMs, agentic AI, evaluation methods, and LLMOps, proactively introducing new techniques where they provide real business value.
  • Mentor and grow other ML scientists, shaping technical culture and raising the overall bar for GenAI rigor and impact across the organization.

• PhD with significant industry experience, or Master’s degree in Computer Science, Machine Learning, AI, or a related quantitative field with 10+ years of applied ML experience, including senior or staff-level technical leadership.
• Deep expertise in Generative AI, LLMs, and agentic AI systems, including hands-on experience designing and deploying production systems.
• Proven ability to design end-to-end AI systems, from problem formulation and data strategy through deployment, evaluation, and long-term ownership.
• Strong software engineering skills in Python; deep familiarity with ML frameworks (PyTorch, Tensor Flow), agentic frameworks (Langgraph, ADK), data systems, and production AIops infrastructure.
• Demonstrated experience influencing technical direction across multiple teams and organizations without direct authority.

• Strong understanding of:
• Agent architectures (planning, tool use, collaboration, orchestration)
• Trade-offs between agentic flexibility and…