Machine Learning Scientist III

We are looking for an experienced Machine Learning Scientist to develop the next generation of personalization and customer understanding systems at Wayfair. You will build large-scale models that learn from billions of customer interactions to improve content recommendations, personalized search, and product discovery. You will also create reusable customer representations that support experiences across the Wayfair ecosystem.

In this role, you will own machine learning solutions end-to-end, from problem definition and hypothesis testing to model development, evaluation, and production deployment. You will partner closely with engineers, product managers, platform teams, and scientists to solve complex challenges in representation learning, ranking, retrieval, and personalization. Your work will translate advances in machine learning research into scalable capabilities used across search, recommendations, marketing, and other customer-facing products, improving relevance and discovery for millions of customers.

Responsibilities:

• Design, train, evaluate, and deploy large-scale machine learning models for customer understanding, personalization, search, and recommendations.
• Evaluate and adapt advances in recommendation systems, representation learning, large language models, and foundation models to solve practical customer and business problems.
• Develop rigorous offline evaluation frameworks and design online A/B tests to measure customer and business impact.
• Collaborate with product, engineering, and analytics teams to translate ambiguous customer needs into well-defined machine learning problems.
• Partner with engineers to productionize models and build reliable, scalable training and inference pipelines.
• Monitor model performance, identify opportunities for improvement, and continuously iterate on models in production.

Requirements:

• A bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 6+ years of relevant industry experience; or a master's degree in a related field with 4+ years of relevant industry experience.
• Proven experience building and deploying machine learning models in production at scale.
• Strong knowledge of machine learning, statistics, optimization, and modern deep learning techniques.
• Experience designing, training, and evaluating models using large-scale datasets.
• Proficiency in Python and experience with machine learning frameworks such as PyTorch or TensorFlow.
• Strong software engineering fundamentals and experience partnering with engineering teams to build reliable, scalable ML systems.
• Excellent written and verbal communication skills, with the ability to explain complex technical concepts to a range of audiences.
• Intellectual curiosity and a commitment to continuous learning.

Nice to have:

• Experience building recommender systems, personalization models, retrieval and ranking systems, or customer understanding models.
• Experience with representation learning, embeddings, contrastive learning, sequence modeling, or transformer architectures.
• Experience with distributed model training and large-scale retrieval and inference pipelines.
• Experience with cloud platforms such as GCP, AWS, or Azure.
• Familiarity with MLOps and workflow orchestration tools such as Airflow, Kubeflow, MLflow, or Vertex AI.
• Experience applying large language models or generative AI techniques to personalization, search, or recommendation problems.