With data-driven product design and our purpose-built, modern end-to-end tech stack, we help shoppers navigate one of the world’s largest catalog of home goods and ensure seamless, dependable deliveries to their homes.
What We Do
A Platform for All Things Home
We are building the preeminent platform for home. We are neither solely a retailer nor solely a marketplace. Instead we take on the harder but more valuable challenge to combine the best of both. We are a platform.
Massive, Global Scale
Our 2,000+ engineers are building the technology that powers an experience for tens of millions of customers every day – and we’re just getting started.
A Nimble, Cutting-Edge Approach
We’re proliferating data science and machine learning across all of Wayfair, automating the end-to-end workflow for building, deploying, and maintaining models at scale.
Our Teams
Build Wayfair’s mobile software ecosystem to support our fastest-growing customer channel.
Build complex consumer-facing and back-end systems that support every element of our business.
Connect our data platforms to guarantee scalability, reliability, and speed.
Guide new ideas from concept to launch and collaborate with engineers and designers to maximize impact.
Identify security flaws, build protective infrastructure, and ensure data privacy as part of a diverse engineering team.
Build and maintain the cloud infrastructure and core production systems.
Manage and support the systems that every Wayfairian uses daily.
Build scalable models that drive our proprietary systems using machine learning, computer vision, and NLP.
Work with cross-functional partners to build thoughtful experiences for our customers, suppliers, and workforce.
Learn about the ins and outs of technology and engineering at Wayfair on our official technology blog.
Changing Tech. Changing Home.
Founded by two engineers, we’ve embraced a culture where we learn from failure, take calculated risks, make smart iterations, and have limitless ambition – all of which have enabled us to pioneer new technologies and set industry standards that are rapidly redefining the way customers shop for their homes. Come join over 3,000 technologists reimagining the e-commerce experience and building homegrown, breakthrough technologies, transforming an industry from product discovery to final delivery.
Fiona says...
“An unwavering customer focus and deep roots in building a strong culture of innovation has helped pave the way for Wayfair as the clear leader in home – and it’s just the beginning. I look forward to being part of an incredible team and building upon this strong momentum as we continuously raise the bar in creating the best possible shopping experience for home.”
Joe says...
“Wayfair has given me the opportunity to not only grow as an employee but as a person. Day in and day out I’m able to take on complex challenges and goals with amazing people from all walks of life. Choosing Wayfair has really been a career-defining decision that I would make over and over again.”
Liliana says...
“My perfect role involves working with great people on a challenging problem, and using the best technology for it. I’ve found all of it on the Wayfair Fraud team, and there is even more in the broader organization. Here, I won’t get bored any time soon.”
What’s New
The Findability Barrier Platform drives a new standard in catalog accuracy, empowering domains to manage product visibility requirements independently for compliance, quality, and market-specific needs. By offering clear paths to resolution for suppliers and internal operation teams, the platform minimizes unfindable products, accelerates issue resolution, and ensures a seamless, high-quality customer experience across Wayfair’s sites.
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When one of our customers has an issue with a product they purchased, they have the option to chat with a customer service agent. Our agents are expected to arrive at a resolution to the customer's issue that balances the customer’s needs with Wayfair’s business needs, all while maintaining a friendly and empathetic attitude. Navigating an interaction with a customer while adeptly applying one of hundreds of Wayfair policies is challenging!
3 Min Read
Kubernetes is a container orchestration tool that helps businesses run their application services smoothly, adjusting resources as needed to handle different levels of activity. However, sometimes it takes time for Kubernetes to notice when there's suddenly a lot more traffic or users.
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Here at Wayfair, we're passionate about creating a seamless and enjoyable shopping experience for every customer. And that experience goes beyond just browsing our vast selection of home goods. This commitment to the customer experience continues even when personalized help is needed – whether they have a quick question about a specific product or want to collaborate with one of our agents to find the perfect pieces for their home. To elevate the service we provide to our customers and empower our sales agents, we've developed an innovative experience powered by cutting-edge generative AI: Wayfair's Agent Co-pilot.
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What is spatial computing?AR, VR, MR, XR - these are all different acronyms to describe the spectrum of a new evolution of computing - one that is heavily visual and that incorporates the space around you into your computing environment.
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Note: This article originally appeared on DataQuest India on January 10, 2024
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In July we launched Decorify, our generative AI model that creates shoppable, photorealistic images to enable consumers to envision their own homes in new styles by simply uploading a picture of their space. The feedback received has been great with users uploading more than 10,000 images and conducting just over 70,000 designs.
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Note: This article originally appeared here on the Google Cloud blog.
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Note: This article originally appeared here on the Google Cloud blog.
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When Wayfair thinks of the cloud, we see a massive world-class “development accelerator” that dramatically reduces the friction for developers to provision, build, and create the things they want without having to toil with the underlying infrastructure to “wire up” those concepts. When we think of cloud innovation, we think of Google, which is at the forefront thanks to its focus on innovation.
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This article is co-authored by Trevor LaViale, Machine Learning Solutions Engineer at Arize AI
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Wayfair is a company that believes growth is built on a strong global presence. Localization is a key element in successfully establishing ourselves in many markets and achieving the desired growth.
5 Min Read
Wayfair brings products to life through digitally created imagery enabled by ML/AI sciences. Over the last several years, we’ve been investing in building 3D capabilities to achieve this at both a scale and cost not possible with traditional photography. Creation of these 3D renders requires significant computation (rendering) capabilities. In May 2022, Wayfair embarked on a Public Cloud Tech transformation journey from our own on-premise data center to the Google Cloud (GCP) which significantly added to our GCP operating costs.
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“Griffin” learns from millions of customer interactions daily to continuously improve customer experience and engagement.
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Last month I attended the Zinnov Confluence – India Edition. For anyone not familiar with the event, it delivers a forum for global brands to share their experiences and perspectives around disruptive technologies, new digital business models, and leadership paradigms that can help bring India’s incredible technology ecosystem into the global community.
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I recently had the pleasure of speaking at Transform 2023 in San Francisco, an event dedicated solely to an innovation that has taken the industry by storm, Generative AI. It was a great event that attracted speakers from a variety of iconic brands, including Google, Johnson & Johnson, Walmart, Fidelity Investments, eBay, FedEx Dataworks, McDonald's Corp, Intel, and the list goes on.
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As a scientist at Wayfair, Sachin Chhabra is helping fulfill the company’s mission of enabling customers to create their own sense of home. To this end, Chhabra utilizes state of the art machine learning techniques to surface relevant product recommendations to customers.
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We recently celebrated International Women’s Month. What does this mean to you? How did you celebrate it?
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One of our missions at Wayfair is to help our 22 million customers find the products they are looking for. For example, when a customer searches for a “modern yellow sofa” on Wayfair, we want to show the most relevant options from the tens of thousands of sofas available in our catalog. To do that, it is important to have a strong understanding of the products we sell. We use machine learning algorithms to analyze and understand the descriptive information (e.g. color, shape) of over 40 million products provided by more than 20 thousand suppliers in our catalog.
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How Wayfair leverages Graph Neural Networks to thwart account-hopping fraudsters
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Last week I participated on a panel at the Milken Institute Global Conference. It was a fantastic event featuring more than 200 sessions, including mine, “Retail Renaissance: Businesses Embracing Technology, Sustainability, and Innovation.”
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In early April, Wayfair introduced the Digital Design Studio in our AllModern Dedham store. The Digital Design Studio (DDS) is a photorealistic interactive experience that lets customers explore Wayfair’s endless catalog by designing 3D furniture arrangements with product cards. Creating this tool was a collaborative effort between Wayfair Next (our R&D group tasked with exploring various emerging tech use cases), Physical Retail, Visual Merchandising, Creative, 3D, and many other Wayfair teams.
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Geo experimentation unlocks incrementality testing when customer-level experimentation is not possible.
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How to make deliveries faster without touching any packages
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Let’s look at how we at Wayfair use custom embedding models to capture complex customer behavior patterns for advanced Fraud prevention.
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Search relevance – the relationship between users’ queries and the products returned in search results – is one of the most important performance indicators for ecommerce storefronts. However, the sheer volume of the data makes evaluating and improving search relevance a difficult proposition. To give just one example, the Wayfair catalog has millions of products, which makes it difficult to create relevance-focused evaluation datasets manually.
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Dive into the ways Wayfair’s data scientists are using Vertex AI to improve model productionization, serving, and operational readiness velocity.
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Wayfair works with over 25,000 suppliers to sell more than 30 million products in a variety of styles from classic to contemporary at compelling price points. In order to create a frictionless shopping experience, we need to enable customers to explore our massive catalog efficiently, and find the right products that help them create their unique sense of home.
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OverviewAs Wayfair technology teams begin their replatforming journeys, more engineers will make decisions for their single purpose database. Choosing the right database technology is one of the most important decisions you can make when building a new microservice. If you realize down the line that you have made the wrong choice, migrating to another database can be a costly and risky procedure. We committed to Google as our cloud platform provider, and they offer multiple robust database options. Learning enough about all of them to make an informed decision can be a steep hill to climb; therefore, we set out to streamline this process, and provide our teams with a guide to help make these decisions.
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To B2B, or not B2B, that is the question.
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