Wayfair

Machine Learning Scientist III - Ads + Search & Recommendations

Seattle, WA, US$194,040-$206,167Posted 1 month ago

Job Description

Salary Range

$194,040 - $206,167 per year. Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the annual base salary only and do not include equity.

Who We Are

The Ads + Search and Recommendations Machine Learning (ML) team 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.

Wayfair’s Advertising business is rapidly expanding, adding hundreds of millions of dollars in profits to Wayfair. We are building Sponsored Products, Display & Video Ad offerings that cater to a variety of Advertiser goals while showing highly relevant and engaging Ads to millions of customers. We are evolving our Ads Platform to empower advertisers across all sophistication levels to grow their business on Wayfair at a strong, positive ROI and are leveraging state of the art Machine Learning techniques.

Wayfair is an online retail platform with the mission to enable everyone to live in a home they love. To do this, Wayfair builds and leverages cutting-edge Machine Learning and AI products and we are looking for talented individuals to join us. You will join the Customer Technology - Search & Recommendations org within its Advertising Footprint and Ranking team working on Candidate Generation and Retrieval Systems. You will be part of a cross-functional, collaborative team driving development of world-class ML systems that drive real-world impact.

Here are some of the key projects our team works on

Candidate Generation/Retrieval

Selecting the right subset of products for our final scoring layers from our entire catalog efficiently through deep understanding of our product catalog and our customers at extreme scale using low latency situations across multiple types of Candidate Generators and Retrieval stages. This includes Generative Retrieval, Lightweight L1 Ranking, item-to-item methods (such as GCNNs), and many more!

Scoring

Producing personalized scores like pCTR and pCVR for downstream ranking and footprint systems through state-of-the-art ML methods including deep learning, sequence transformers and LLMs ([1] WaySeq)

Ranking Optimization

Ordering Advertising products on the page given personalization, relevance and other factors ([2] Profit Aware Ad Ranking)

Footprint

Determining where Advertising goes on our website by optimizing the tradeoff between ad revenue and customer relevance ([3] Ad Allocation at Scale)

What You’ll Do

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 Ads KDD, NeurIPS and RecSys.

You Are a Fit If You Have

Minimum 2+ years of experience with PhD, or 4+ years of industry experience with MS, or 6+ years of experience with a BS in a quantitative STEM field.

1+ years of industry experience as an ML engineer, applied scientist, or research scientist, with a proven track record of delivering ML projects autonomously in recommendations, search, or ranking.

Expertise in recommendation systems, including candidate generation, ranking algorithms, and user-item modeling.

Deep understanding of techniques like sequence modeling, GCNNs, and/or 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 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.

Why You’ll Love Wayfair

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 ClassPass, and various regional gym memberships)
Mental Health Support (Global Mental Health, Global Wayhealthy 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 Support
Parental Leave
Global Surrogacy & Adoption Policy
Professional Development & Recognition:
Rewards & Recognition
Global Employee Anniversary Awards
Paid Volunteer Work
Unique Perks:
Employee Discount
U.S. Bluebikes Membership
Global Pod Outings
Work/Life Balance:
Emphasizing a supportive & flexible work environment that encourages a balance between personal and professional commitments

If you don’t meet every qualification listed, we still encourage you to apply. We’re looking for strong team players who can learn, grow, and make an impact.

This is a hybrid position located in Seattle, WA. The team is in-office Tuesday-Thursday and remote on Monday and Friday.