Machine Learning Scientist III, Recommendations

About This Role

We are looking for an experienced Machine Learning Scientist III to join our content recommendations team. In this role, you will be at the core of building and optimizing ML-based recommender systems (e.g., image and content recommendations, homepage and email optimization and personalization) to enhance the customer experience r work will directly impact how millions of customers discover and engage with products, driving significant business value.

As part of Wayfair's SMART (Search, Marketing, and Recommendations Technology) team, you will collaborate with ML scientists, engineers, and product teams to develop and deploy cutting‑edge recommendation models that operate s role is an opportunity to solve complex problems related to personalization, large‑scale machine learning, latency, and scalability while leveraging state‑of‑the‑art AI techniques.

• Develop and optimize recommendation models that power personalized experiences across Wayfair’s site, app, email, and push notifications.
• Conduct applied research to improve recommender systems using traditional ML techniques, deep learning and reinforcement learning.
• Build scalable ML pipelines for training, evaluation, and inference, ensuring models operate efficiently in production.
• Work closely with engineering teams to deploy models in a production environment, addressing real‑world constraints such as latency, interpretability, and scalability.
• Analyze model performance and iterate based on A/B test results, offline evaluation metrics, and business impact.
• Leverage and contribute to open‑source ML frameworks while staying up to date with cutting‑edge research in recommendation systems.
• Drive innovation by identifying opportunities to improve personalization strategies and developing novel algorithms that enhance customer engagement.
• Collaborate with cross‑functional teams including product managers, software engineers, and data scientists to align ML objectives with business goals.
• Mentor other less–experienced scientists.

• 5+ years of experience developing and deploying machine learning models, with a focus on recommendations, ranking, or personalization.
• Strong theoretical understanding of machine learning and deep learning applied to large‑scale recommendation problems.
• Experience in training, evaluating, and optimizing recommendation models in production, leveraging techniques such as collaborative filtering, sequence modeling, representation learning and multi‑armed bandits.
• Proficiency in Python and experience with ML frameworks such as Tensor Flow, PyTorch, or Scikit‑Learn.
• Familiarity with big data processing (Spark, Hadoop) and ML pipeline orchestration (Airflow, Kubeflow, MLflow).
• Strong coding skills and familiarity with building scalable ML systems in cloud environments (AWS, GCP, Azure).
• Ability to design experiments and analyze results using A/B testing and statistical techniques.
• Excellent communication skills, with the ability to explain complex ML concepts to non‑technical stakeholders and drive data‑driven decisions.

• Experience developing core recommendation systems for eCommerce, marketplaces, or streaming platforms.
• Familiarity with reinforcement learning or contextual bandits for adaptive recommendation strategies.

This role offers the opportunity to work on high‑impact ML problems at scale, shaping the future of personalization and recommendations  you're passionate about building intelligent systems that enhance customer experiences, we’d love to hear from you!

• Time Off:
  • Paid Holidays
  • Paid Time Off (PTO)
• Health & Wellness:
  • Full Health Benefits (Medical, Dental, Vision, HSA/FSA)
  • Life Insurance
  • Disability Protection (Short Term & Long Term Disability)
  • Global Wellbeing:
    Gym/Fitness discounts (including US Peloton, Global Class Pass, and various regional gym memberships)
  • Mental Health Support (Global Mental Health, Global Way healthy Recordings)
  • Caregiver Services
• Financial Growth & Security:
  • 401K Matching (Employee Matching Program)
  • Tuition Reimbursement
  • Financial Health Education (Knowledge of Financial Education - KOFE)
  • Tax Advantaged Accounts
• Family Support:
  • Family Planning…