Wayfair is moving the world so that anyone can live in a home they love – a journey enabled by more than 3,000 Wayfair engineers and a data-centric culture. The Customer Technology(CT) team builds and owns all the Machine learning (ML) products that power all search, marketing, and recommendations technology across Wayfair. Our algorithms tackle a varied & broad spectrum of challenges in the Wayfair marketplace; from empowering suppliers to easily add products to our catalog, to enabling our customers to discover and purchase a vast & diverse assortment of home goods, to driving marketing experiences customers love all at web scale.
We are looking for machine-learning scientists for research and development of the ML systems that power our real-time
Search Typeahead, Search Guidance, Query Understanding & Interpretation, Query Refinement and Recommendations, and emerging GenAI-powered search capabilities
. In this role, you’ll partner with fellow scientists, engineers, analysts, and product managers to apply science and machine learning skills to directly impact Wayfair’s revenue.
What you’ll do
Design, build, deploy and refine large-scale machine learning models and algorithmic decision-making systems that solve real-world problems for customers
Work cross-functionally with commercial stakeholders to understand business problems or opportunities and develop appropriately scoped analytical solutions
Collaborate closely with various engineering, infrastructure, and ML platform teams to ensure adoption of best-practices in how we build and deploy scalable ML services
Identify new opportunities and insights from the data (where can the models be improved? what is the projected ROI of a proposed modification?)
Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on.
What you’ll need
3+ years of industry experience with a Bachelor/ Master’s degree or minimum of 1-2 years of industry experience with PhD in Computer Science, Mathematics, Statistics, or related field.
Strong theoretical understanding of statistical models such as regression, clustering and ML algorithms such as decision trees, neural networks, etc.
Solid understanding of algorithms for search retrieval, ranking, relevance, query understanding, NLP, or recommender systems.
Good proficiency in the Python ML ecosystem (pandas, NumPy, scikit-learn, XGBoost, etc.).
Solid hands-on expertise deploying machine learning solutions into production
Strong written and verbal communication skills.
Intellectual curiosity and enthusiasm about continuous learning.
Nice to have
Experience in e-commerce, or online search systems.
Knowledge of modern NLP techniques (transformers, embeddings, semantic search).
Experience applying Generative AI / LLMs (e.g., GPT, LLaMA) to real-world problems such as search, personalization, or content generation.
Experience with deep learning frameworks like PyTorch.
Familiarity with vector databases, information retrieval frameworks (e.g., Elasticsearch, Vespa)
Familiarity with GCP (or AWS, Azure), ML model development frameworks, ML orchestration tools (Airflow, Kubeflow or MLFlow)
Experience with Apache Spark Ecosystem (Spark SQL, MLlib/Spark ML).
ATS Match is available
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