People and technology making products available
where and when they are most needed
At Alloy.ai, we help brands operate more efficiently based on actual demand. When consumers buy goods like shampoo, chocolate, or headphones, it leaves a trace – sales are tracked and inventory levels change. Our cloud-based platform automatically connects and analyzes this data, which is siloed today across hundreds of channels from e-commerce to stores, suppliers, and 3PLs.
Engineers at Alloy.ai work on enabling end-to-end visibility of the supply chain, from sales and inventory data at the retail level all the way to individual shipments and orders. The real world is complex, and so are our engineering challenges. Our work spans across processing terabytes of data to supply chain modelling challenges and ultimately to building intuitive applications that leave users with clear, actionable insights.
Speaking as a data scientist, the data we have to work with at Alloy.ai and the potential value we can create from it — in-depth descriptive analytics, prediction of demand and shortages, and prescriptive actions — are very exciting.
At startups, especially early stage ones like Alloy.ai, the projects span many areas and you have the opportunity to really shape the direction of the product. It’s really exciting to have such a major impact and know that it probably wouldn’t have happened without you.
Within Alloy.ai, there is such a vast amount of data that you need to distill into high-level insights for the user; it’s an interesting challenge to build a platform that gives you those key takeaways, but also lets you zoom into the details when needed.
I am really excited about all the challenging technical problems we have at Alloy.ai. We deal with very large and complex data sets, which come with a lot of interesting modeling and performance problems, as well as fun algorithmic challenges that we get to solve.
I try to understand what each person’s goals are, personally and professionally. Then I match them with projects and partners that I feel will push them and give them opportunities to grow in the ways they care about. When someone on my team has a question or problem, I try to give them tools rather than answers.
There is no guidebook to build the application we are working on at Alloy.ai. We have to use close relationships with our customers, flexible systems, and nimble mindset to try different approaches until we find the best option. This culture of experimentation and iteration makes coming to work every day interesting and exciting.
At Alloy.ai, we tackle many complex problems related to the amount of data we store and analyze. What makes it especially fun and interesting is the team behind the solutions. Being able to bounce ideas off each other, learn from my peers and work together towards potential solutions is what gets me excited about work.
Speaking as a data scientist, the data we have to work with at Alloy.ai and the potential value we can create from it — in-depth descriptive analytics, prediction of demand and shortages, and prescriptive actions — are very exciting.
At startups, especially early stage ones like Alloy.ai, the projects span many areas and you have the opportunity to really shape the direction of the product. It’s really exciting to have such a major impact and know that it probably wouldn’t have happened without you.
Within Alloy.ai, there is such a vast amount of data that you need to distill into high-level insights for the user; it’s an interesting challenge to build a platform that gives you those key takeaways, but also lets you zoom into the details when needed.
I am really excited about all the challenging technical problems we have at Alloy.ai. We deal with very large and complex data sets, which come with a lot of interesting modeling and performance problems, as well as fun algorithmic challenges that we get to solve.
I try to understand what each person’s goals are, personally and professionally. Then I match them with projects and partners that I feel will push them and give them opportunities to grow in the ways they care about. When someone on my team has a question or problem, I try to give them tools rather than answers.
There is no guidebook to build the application we are working on at Alloy.ai. We have to use close relationships with our customers, flexible systems, and nimble mindset to try different approaches until we find the best option. This culture of experimentation and iteration makes coming to work every day interesting and exciting.
At Alloy.ai, we tackle many complex problems related to the amount of data we store and analyze. What makes it especially fun and interesting is the team behind the solutions. Being able to bounce ideas off each other, learn from my peers and work together towards potential solutions is what gets me excited about work.
Alloy.ai is a distributed organization across three sites. As such, our culture relies heavily on open, asynchronous communication. We write tickets with lots of detail, specs and business context on customers – it’s all available for our engineers!
Promoting and taking individual ownership is highly valued at Alloy.ai. Our engineers are not held back by a bureaucratic management structure but operate in a just-in-time mode. We ship fast based on priorities established in the product design stage and conduct in-depth code reviews both within and across teams. We have regular knowledge sharing sessions as well as hackathons, and some of those projects even make it all the way to the product.
Our engineers develop a deep understanding of how the global supply chain works over time, which is thrilling – the next time you see a product on a department store’s shelves, you’ll never think about it the same way! You’ll be thinking about how that product is being sold, how it got there and where it was manufactured.
We are constantly on the lookout for engineering talent. We’d love to hear from you – our open positions in engineering are listed below. Our technical interview process gives you a hands-on glimpse of Alloy.ai – in the course of the interviews, you’ll be building parts of Alloy.ai piece by piece that we’ve also built before. Read Interviewing at Alloy.ai for more info.
Technical discussion on how we made Cypress end-to-end tests stable and maintainable at Alloy.ai.
Keep readingTechnical discussion on how we migrated our frontend codebase to use vanilla TS types for core data structures instead of immutable.js types.
Keep readingHow we migrated our whole frontend codebase from JavaScript + immutable.js to TypeScript in one go.
Keep readingHow Alloy leverages BigQuery's built-in weather data to power analytics features.
Keep readingHow we sped up our backend integration test suite 50% faster with a couple lines of code by speeding up database instantiation.
Keep readingInterview of two of our interns, who work on production code and get to contribute code that is shipped directly to our customers.
Keep readingInterview of two of our interns, who work on production code and get to contribute code that is shipped directly to our customers.
Keep readingApplication and Data
Infrastructure
Business Tools
