Machine Learning Scientist II - Catalog Science

Salary Range: $176,000 - $184,250 per year. Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the annual base salary only and do not include equity.

The Catalog Science team at Wayfair drives growth by tackling fundamental challenges ranging from multi-modal product understanding, product relationships modeling, to GenAI‑driven catalog intelligence  team provides the core capabilities that are crucial for enhancing the customer browse experience (e.g., visual search, recommendations), streamlining supplier product onboarding (e.g., product classification, image and content tagging), informing catalog competitiveness, and ultimately delivering significant impact for our customers, suppliers, and Wayfair.

• Research and experiment with state‑of‑the‑art multi‑modal understanding techniques and algorithms. Design and implement evaluation strategies applied to real‑world scenarios tailored to Wayfair use cases.
• Leverage and fine‑tune LLMs (e.g., OpenAI GPT, Google Gemini, Anthropic Claude, Open Source) to build AI‑driven classifiers, product taggers, and quality control mechanisms.
• Develop and refine the visual search system leveraging cutting‑edge technologies in computer vision, vision language models, and the large‑scale data orchestration.
• Implement AI‑powered automation for product data structuring, attribute extraction, and metadata validation—ensuring our catalog remains accurate, complete, and scalable.
• Collaborate with top AI research and industry leaders (e.g., Google, Anthropic, Snorkel AI) to explore cutting‑edge techniques in LLMs, data labeling automation, and scalable ML workflows.
• Develop agentic AI workflows for automated schema definition, dataset generation, production relationship modeling, and LLM‑based judgment systems to validate catalog data.
• Partner with cross‑functional teams across engineering, scientists, and product to ensure AI solutions integrate seamlessly into catalog systems.
• Optimize cost, efficiency, and scalability of AI models, leveraging parameter‑efficient fine‑tuning (LoRA, QLoRA), knowledge distillation, and hybrid ML approaches.

• PhD in Computer Science, Machine Learning, Electrical Engineering, Physics, or a relative field OR MS and 2+ years of full‑time experience.
• Deep understanding of traditional machine learning and deep learning techniques, reinforcement learning, and multi‑modal understanding.
• Deep understanding of LLMs/VLMs, generative AI, and techniques including fine‑tuning, RAG, etc.
• Hands‑on experience using models like GPT, Gemini, Claude, and/or open‑source alternatives in research or production environments.
• Professional coding expertise in languages like Python or Go, proficiency in SQL, and experience with data visualization tools; skilled in using ML frameworks (Tensor Flow, PyTorch) and implementing CI/CD, containerization, and version control best practices (git).
• Experience with data engineering concepts scalable data collection, processing, and transformation.
• Excellent communication skills, with the ability to clearly articulate complex AI concepts to non‑technical stakeholders while collaborating across teams.
• Ability to quickly learn new tools and techniques in a fast‑paced, evolving environment, while managing multiple priorities with a high level of attention to detail and staying current with the latest ML research.
• Track record of delivering successful machine learning projects from conception to production, demonstrating strong deployment, problem‑solving, and maintenance skills.

• Familiarity with MLOps, cloud infrastructure, and engineering best practices (Google Cloud Platform, Airflow/Composer, Kubeflow, MLFlow, Kubernetes, Vertex
  
  AI, Spark, Data Dog, Arize).
• Experience working in e‑commerce catalog AI systems, retail data structuring, or large‑scale product classification.
• Research publications in deep learning, computer vision, or generative AI.
• Experience with autonomous AI agents, reinforcement learning, or online learning systems.

• Time Off:
  Paid Holidays, Paid Time Off (PTO).
• Health…