Machine Learning Scientist II

What you’ll doDesign, build, deploy and refine large-scale machine learning/AI models and algorithmic decision-making systems that solve real-world problems for customersWork cross-functionally with engineers, analysts, product managers and commercial stakeholders to understand business problems, opportunities and develop appropriately scoped ML solutionsCollaborate closely with various infrastructure, and ML platform teams to develop production grade services that follow best-practices in how we build and deploy scalable ML servicesBuild cutting edge robust ML models and systems that directly impact our customers every dayIdentify 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 needBachelor's degree with 3-5 years or Master’s degree with 2-4 years of experience in Computer Science, Mathematics, Statistics, or related field Proficiency in Python or one other high-level programming language.A strong theoretical understanding and solid hands-on experience working with machine learning algorithms.Classical ML and deep learning techniques across NLP, sequence modeling, RL, Recommendation systems, clustering, and time series.Experience with Python ML ecosystem (numpy, sklearn, pytorch, XGBoost, etc.)Strong written and verbal communication skills, ability to synthesize conclusions for non-experts, and overall bias towards simplicity and efficiencyIntellectual curiosity and enthusiasm about continuous learning

Nice to haveDesigning end-to-end ML solutions from prototype to production.Experience in deploying ML workflows/models in production systemBuilding or experimenting with LLMs, retrieval systems, generative models, and agentic AI workflows Familiarity with cloud based compute approaches GCP (or AWS, Azure), ML model development frameworks, and ML orchestration tools (Airflow, Kubeflow, VertexAI, etc)Experience with Kubernetes and DockerClear track record of publishing or work related projects showing ability to develop cutting edge AI systems.

Who we areWayfair 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 machine learning products that power search, marketing, personalization, and recommendations 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 Customer understanding and personalization systems - Customer embeddings, Recommendation systems, user interest prediction, generative recommendation systems, short term and long term customer preference identification. In this role, you’ll partner with fellow scientists, engineers, analysts, and product managers to apply machine learning skills to directly impact Wayfair’s revenue.