Wayfair is looking to enhance the customer experience by optimizing ML-based recommender systems for content and product discovery, driving significant business value through personalization and engagement.
Requirements
- 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 TensorFlow, 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.
Responsibilities
- 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.
Other
- Mentor other less experienced scientists on the team
- 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.
- All Mountain View-based interns, co-ops, and corporate employees will be in office in a hybrid capacity. Employees will work in the office on designated days, Tuesday, Wednesday, and Thursday, and work remotely the other 2 days of the week.
