We build a machine vision solution that allows automatic and instant checkout in stores. Shoppers go to a store, grab what they want, and then leave. No lines and no scanning.
We have a ridiculous number of hard and interesting problems to solve.
We're changing the way the world shops, and the opportunity is crazy big. Over $100B+ spent on checkout every year.
We're funded by YC and other well-known funds, including CRV, Social Capital, Initialized, SVAngel.
We use a 3 month cycle called Arcs. At the beginning of the arc we sit as a team and prioritize large scale problems, priorities, and features that we need to get done, then we figure out what we want to get done in the next 3 months. After that we figure out the best way to organize. Sometimes that means multiple small teams, sometimes it means team-wide standups, but whatever it is we tailor our cadence and organization to what will be best for our 3 month goals, rather than finding goals that are best suited for our current organizational structure.
Day to day we use github, pull requests, code reviews, and testing, like most sane dev teams.
We're building a real time deep learning inference engine that has to stream process dozens of camera feeds on a distributed GPU cluster. Keeping this system performant and fault tolerant is a massive challenge.
We're installing a GPU cluster in every store we work with. Monitoring and maintaining these remote systems is a challenge now, but will be a huge challenge as we scale to more stores.
Our backend database has to work in both a fractured state for each store, as well as in a global state, aggregated across all stores. If the internet goes down in a store, our in-store system needs to keep running. Design and scaling this system is an ongoing challenge.
We have to ingest video data of every unique item we encounter in the world. Some stores have 100,000+ unique items. Collecting, maintaining, and serving this data to train machine learning algorithms, especially in a massively distributed setting, is an open challenge.
We have a complex, multi-model machine learning pipeline. When something goes wrong or a model makes an incorrect prediction we need to understand why. Building tools to get insight into this complex chain is a challenging intersection of tooling, UX, and ML.
We're building apps that will be used by shoppers around the world. Perfecting their shopping experience is the heart of everything we're trying to achieve.
Work on a distributed database system, that has to be deployed both locally to every store we install in, as well as have a globally consistent view across all stores.
Work on a real time, streaming, distributed, deep learning inference pipeline, with challenging inter-node dependencies. We're pushing the limits of what these technologies can do in real time.
Build tools that help bridge the gap between machine comprehension and human intelligence.
We value independence, candor, and grit. We're solving hard problem and we value people that love taking ownership and drilling into finding a solution. We also value and encourage failure. Most approaches to the problems we work on will fail. Sharing those failures is an important way to move the entire team forward. Because of that we value people's implementation of approaches, not whether those approaches pay off.
We offer a $5,000 relocation stipend if you need to move.
EatClub lunch is provided everyday. It's pretty awesome.
10am to 4pm are our core hours, how you work otherwise is up to you.
We encourage everyone to attend one or two conferences a year, covered by the company.
Full medical, dental, vision.
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