About the role
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, and 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.
Responsibilities:
- Design, build, deploy, and refine large-scale machine learning/AI models and algorithmic decision-making systems that solve real-world problems for customers.
- Work cross-functionally with engineers, analysts, product managers, and commercial stakeholders to understand business problems and opportunities and develop appropriately scoped ML solutions.
- Collaborate 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 services.
- Build cutting-edge, robust ML models and systems that directly impact our customers every day.
- 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.
Requirements:
- Bachelor's degree with 3-5 years or master's degree with 2-4 years of experience in computer science, mathematics, statistics, or a 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 the 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 efficiency.
- Intellectual curiosity and enthusiasm about continuous learning.
Nice to have:
- Designing end-to-end ML solutions from prototype to production.
- Experience in deploying ML workflows/models in production systems.
- Building or experimenting with LLMs, retrieval systems, generative models, and agentic AI workflows.
- Familiarity with cloud-based compute approaches (GCP, AWS, Azure), ML model development frameworks, and ML orchestration tools (Airflow, Kubeflow, Vertex AI, etc. ).
- Experience with Kubernetes and Docker.
- Clear track record of publishing or work-related projects showing ability to develop cutting-edge AI systems.