Machine Learning Scientist, Wayfair
Search
$176-187k
+ Restricted stock units
A global online homeware marketplace
Job no longer available
A global online homeware marketplace
1001+ employees
Job no longer available
$176-187k
+ Restricted stock units
1001+ employees
Company mission
To help everyone, anywhere create their feeling of home.
Role
Who you are
- Bachelor's or advanced degree (Master’s, PhD) in Computer Science, Machine Learning, Mathematics, Statistics, or related field
- 4+ years of industry experience as an ML engineer, applied scientist, or research scientist, with a proven track record of delivering ML projects autonomously and driving measurable business impact, particularly in recommendations, search, or ranking
- Expertise in recommendation systems, including candidate generation, ranking algorithms, and user-item modeling
- Deep understanding of collaborative filtering, sequence modeling, and embedding-based personalization
- Experience with end-to-end project ownership, including collaboration with business partners and strong written and verbal communication skills
- Strong proficiency in Python and/or Java for building and deploying ML-driven recommendation systems
- Experience deploying machine learning models in production environments, with a focus on cloud-based solutions such as GCP (BigQuery, GCS, Vertex AI, Composer), as well as workflow orchestration tools like Airflow, model tracking using MLflow, and containerization technologies like Docker
What the job involves
- The Search and Recommendations Machine Learning (ML) team is at Wayfair is at the forefront of designing and implementing algorithms that define our global search experience
- Using cutting-edge machine learning approaches—including deep sequence models, embeddings and multi-modal technologies—we aim to deliver a personalized and compelling experience for over 22 million active customers
- Our mission is to make the user experience intuitive and efficient, helping customers discover exactly what they need in a vast and diverse product catalog
- Elevate recommendation quality: Design and implement scalable machine learning models to improve the ranking, and personalization of product recommendations across Wayfair
- Develop robust retrieval systems: Build and optimize candidate generation pipelines that surface high-quality, personalized product recommendations at scale
- Leverage user and product signals: Apply deep learning and representation learning to model user preferences, product attributes, and contextual signals for better recommendation performance
- Innovate with cutting-edge techniques: Explore sequence modeling, embeddings, and multi-modal modeling to drive the next generation of recommender systems
- Collaborate cross-functionally: Partner with product managers, engineers, and data scientists to align recommendation strategies with business objectives and user needs
- Tackle recommendation-specific challenges: Solve key issues such as the cold-start problem, data sparsity, product compatibility and seasonality in dynamic environments
- Advance the ML community at Wayfair: Contribute to internal knowledge sharing, author technical documentation, and represent Wayfair at top ML conferences like NeurIPS and RecSys
Our take
Wayfair emerged in the early era of eCommerce with a mission to revolutionize online shopping, offering customers a convenient platform to purchase goods. Today, it stands as one of the foremost global players in the online furniture delivery industry, boasting an impressive inventory of over 33 million products.
Renowned for its extensive product range and comprehensive service offerings, Wayfair distinguishes itself by providing an end-to-end customer experience, from browsing to doorstep delivery. Despite its prominence, the company faces profitability challenges attributed largely to expansion expenses. Nonetheless, its solid presence in the competitive online homeware sector solidifies its position as a key contender.
With ambitious global expansion plans, Wayfair remains committed to maintaining its leadership in the industry. As it aspires to become the ultimate destination for all home needs, its more recent ventures into physical retail represent significant strides toward this overarching goal.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
-14% employee growth in 12 months
Company
Company HQ
Prudential / St. Botolph, Boston, MA
Leadership
Niraj Shah
(Co-Founder & CEO)Studied Engineering at Cornell University before co-founding Spinners, a Boston-based IT services company. Previously acted as Entrepreneur in Residence for Greylock and has served as CEO of Wayfair since co-founding the company in 2002.
Steven Conine
(Co-Founder)Co-founded Spinners before working for Operations at iXL. Conine also co-founded Pillar VC in 2016.
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