As a Senior ML Scientist at Wayfair, you'll shape the future of how millions of customers discover, explore, and shop for their homes through intelligent, scalable AI systems.
Who We Are
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) science 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 an experienced Senior Machine Learning Scientist to lead the development of the next generation of intelligent customer experiences at Wayfair. You will design and deploy large-scale machine learning systems that power personalization, recommendations, search, marketing decisioning, GenAI experiences, and other customer-facing products.
Working closely with engineers, product managers, platform teams, and fellow scientists, you will own highly ambiguous technical problems from research through production. You will shape technical strategy, mentor scientists through technical leadership, and help bring state-of-the-art machine learning research into production for millions of customers. The solutions you build will become foundational capabilities that are reused across multiple customer technology domains, enabling personalization and intelligent decision-making throughout the Wayfair ecosystem.
What You'll Do
- Lead the design, development, and deployment of large-scale machine learning systems across personalization, recommendations, search, ranking, generative AI, and customer decisioning.
- Drive technical strategy and architecture for foundational ML capabilities that are reused across multiple customer-facing products.
- Evaluate and adapt the latest advances in recommendation systems, representation learning, large language models, reinforcement learning, and foundation models to solve real-world customer problems.
- Partner closely with engineering and platform teams to productionize scalable ML systems and establish best practices for model training, inference, monitoring, and lifecycle management.
- Lead experimentation through offline evaluation, online A/B testing, and long-term measurement of business impact.
- Influence technical direction across multiple teams through architecture reviews, technical design, and cross-functional collaboration.
- Mentor scientists and engineers through technical guidance, design reviews, and scientific leadership.
- Contribute to Wayfair's broader applied science community by raising technical standards and sharing best practices.
What You'll Need
- PhD in Computer Science, Mathematics, Statistics, or a related field with 5+ years of relevant industry experience, or a Master's degree with 9+ years of relevant industry experience, or a Bachelor's degree with 10+ years of relevant industry experience building and deploying large-scale machine learning systems.
- 4+ years of experience providing technical leadership for large-scale machine learning systems.
- Strong theoretical understanding and practical experience applying statistical, optimization, deep learning, and machine learning techniques, including transformer-based models, to production systems at scale.
- Proficiency in Python and modern ML frameworks such as PyTorch or TensorFlow to develop production-grade machine learning systems.
- Demonstrated ability to influence technical strategy, drive cross-functional alignment, and shape multi-quarter technical roadmaps.
- Intellectual curiosity and a desire to always be learning!
Nice to have
- Experience building personalization, recommendation, retrieval, ranking, optimization, or customer decisioning systems.
- Experience with representation learning, embeddings, transformer architectures, sequence models, or foundation models.
- Experience applying LLMs, generative AI, or reinforcement learning to customer-facing products.
- Experience building large-scale distributed training, retrieval, and inference systems on cloud-native ML platforms.
