MTailor

< 10 Employees
< 10 Engineers
$5M - $10M Funding
Series A

MTailor sells custom clothing by measuring you with your phone's camera on iOS and Android. MTailor’s computer vision technology is 20% more accurate than a professional tailor. We can deliver your entire wardrobe - jeans, dress shirts, suits, and even t-shirts.

MTailor is the first easy and accessible way to experience the luxury of custom clothing. At the same price as many mainstream off-the-rack clothiers (e.g., J. Crew, Brooks Brothers, Ralph Lauren) and with the convenience of an app, you can get clothing made to fit you perfectly, instead of clothing made to fit someone else.

We're a very full-stack company; we market our own brand direct to consumers, design our own proprietary vision and fit algorithms, and we even own and run our own factory.

MTailor photo 1 MTailor photo 2 MTailor photo 3

Why join us?
  • Buying clothes online is uniquely hard due to fit; companies make returns easy, but fundamentally, returning something is high friction.

    Our at-home fit technology for custom clothing enables an online clothes shopping experience that actually feels convenient; you can be confident that you will like the clothes that show up (and avoid the hassle of a return).

    Your MTailor clothes will fit as well or even better than what you would get in a store, without needing to visit a mall or try anything on.

  • Our product has natural data network effects; with every new customer, we can improve our fit algorithms and get closer and closer to a perfect fit, every time. The more we build up this dataset, the harder it becomes to copy.

    Average ecommerce clothing return rates are ~25%. When we started, our shirt return rate was > 35%, and it is now < 13% (and still going down). Long-term, we are aiming for a return rate of < 5%.

  • We were profitable over the last year.

  • Without any new funding since 2016, we grew 80% in 2017 and again 80% in 2018.

  • Over time, the business has improved dramatically (e.g., per customer revenue is up over 30%, in-app yield up 50%), and we still have a long list of projects to continue improving the core business.


Engineering at MTailor
Engineering team and processes

Currently, we have an engineering team of 2, not including CEO / founder who was once technical. One engineer focuses on the fit algorithms and our backend infrastructure, while the other one focuses on our user facing software, primarily the iOS mobile app. New initiatives are typically driven by the CEO, working in close collaboration with our engineers. Because of our small team, the engineers take a active roles in product management and product (and algorithm) strategy.

When doing any engineering, we always keep an eye on what will benefit the customer, and try to minimize engineering workload and maximize benefit to the customer.

Technical Challenges

We have a unique machine learning problem (deliver the perfect fit) with a proprietary and growing dataset. The data can be noisy (e.g., what clothes the person wears while getting measured), the feedback is imperfect (we have to guess what clothing corrections the customer desires), and fit could depend on many other factors (e.g., the garment's fabric, the manufacturing tolerances). So solutions for improvement are frequently non-obvious.

We utilize the camera and accelerometer APIs on iOS and Android in novel ways to create a deceptively easy user measurement experience.

To optimize our manufacturing processes, we build a lot of our own supply chain software. Standard supply chain software assumes production of one or a few fungible product(s) produced many times (e.g., 10,000 copies of the same shirt). That is a poor assumption for making custom garments on-demand. We've built everything from a backend for our factory to specialized customer support capabilities.

Projects you might work on
  • Investigate various ways to improve our measurement prediction algorithms, such as trying new ML architectures and adding new sources of signal (e.g., iOS depth data). You would end up doing a combination of coding, out-of-the-box thinking, cross-functional collaboration, and mathematical analysis. This project is about improving our core brand promise of a perfect fit.

  • Update and add new features to our customer support tooling, such as integrating stripe for faster refunds. Because everything is made bespoke, we build 1:1 relationships with many of our customers, and our customer support team is the front line of building those relationships.

  • Work with our apparel patternmaker to add new dimensions of fit (new measurements) to our clothing and improve our fit. With new measurements in a garment, we can solve customer fit problems that couldn’t previously be addressed.

  • Analyze usage patterns in our app and purchasing behavior to more effectively guide product decisions and advertising spend.

  • Own our backend systems and extend its capabilities to improve our production capabilities. One small example would be to update our inventory system so that our tuxedo fabric inventory and suit inventory (which share the same underlying fabric) are unified; currently, our system can list our black tux as out-of-stock and our black suits as in-stock, even though we make both of them with the same fabric.

Tech stack
Node.js
Python
JavaScript
Android
iOS Development
MongoDB
Tensorflow
React

Working at MTailor

We value:

  • Testing and Data - we like to test ambitious hypotheses with the lowest amount of effort

  • The Customer - we always start with what the customer wants (for both new features and product simplifications)

  • An Excellent Work / Life Balance - everyone has a life outside of work (and we encourage that); we are focused on results, not time in the office

  • Self-Motivation - it is frequently up to you to design and execute new initiatives

  • Collaboration and a friendly environment (we hate politics)

  • Building a great, sustainable business

Generous Vacation

MTailor has an unlimited vacation policy, and we encourage vacations. The CEO took more than 35 days (including holidays) off in 2018.

Flexible Hours

Everyone tends to be in the office 11ish - 4ish, but most people set their own schedules. Some people arrive early and leave early, some people arrive late and leave late. And whenever you need to work from home or remotely, that's ok. We care about results, not time in the office.

Work from Home

We do not have a strict enforcement of work in the office. People generally work from home whenever it's more convenient and productive to.

Team Activities

We do at least one team event per month, like an escape room, axe throwing, or mini golf.


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