About the role
About Radar Radar is the global leader in geolocation, with geofencing SDKs, maps APIs, and AI-enabled solutions for marketing, fraud, and operations teams. Why is Radar the best place to work? We're trusted by some of the world's best companies, from high-growth startups to the Fortune 500. We have incredible scale: We're processing over 1 billion API calls per day from hundreds of millions of devices. We're well-resourced, and we've raised $85.5M from world-class investors, including Accel and Insight Partners. We have a high-performance culture, with ambitious and entrepreneurial teammates in every role. We recently moved into an amazing new office in Flatiron, Manhattan, NYC. We were recently named a top 10 best place to work in NYC by Crain's. Despite our growth and scale, we're still just getting started. That's where you come in.
About
the role We're looking for Product Engineers to build machine learning-based systems into core Radar products. This is a product-oriented role building new machine learning-based systems into our backend, data infra, and mobile SDKs. The ideal engineer for this role is someone who is primarily an ML engineer but wants to broaden their skills into other stacks like server, data and mobile. The perfect candidate will see themselves as a generalist who has built real ML systems and is ultimately motivated by driving impact to products and customers by building end-to-end features that leverage machine learning. We have many ML challenges across our Geofencing, Maps and Fraud products.
How we work: Most of our engineering team are former technical co-founders or former Radar interns from schools like Waterloo and CMU. Most engineers at Radar fit one of two molds, technically: either Staff level expertise in one stack, or "Multi-Stack" at any level. We say "Multi-Stack" because "Full-Stack" has the connotation of "Frontend and Backend", but Radar Engineers might also work on Mobile or Data engineering. Not that you need to be an expert in all of those, but a desire to learn, jump around to different stacks, and get things done is the important part. We care a lot about shipping fast and talking to customers. We're committed to our product vision of full-stack location infrastructure, but we also know that customer feedback is a treasure map to gold. Even though Slack is the brain of our company, working together in-person in our NYC HQ is the fastest way for us to get things done. We meet on Mondays to plan out work for the week in small groups and use Linear for planning. To us, a week is a long time, and we expect to ship big things every week. The stack: We have systems that leverage LightGBM and random forests using scikit and Rust and we need to build out new systems impacting additional products. The server is a TypeScript Node.js app and a Geospatial Rust database we built called HorizonDB. We use MongoDB, S3/Athena, Redis, Airflow and everything is deployed to AWS. Most engineers are in the on-call rotation. We sponsor OpenStreetMaps, MapLibre, and OpenAddresses. How we use AI: Engineers choose what AI tools they use, Claude and Codex being the most popular. We're actively building Claude skills - for example we've taught it how to debug HorizonDB, our geospatial database. All code changes are reviewed by an Engineer knowledgeable in that area. Claude and Codex also review all PRs. There is a range of how much engineers use AI. Most use it daily if not weekly. We are excited about what AI can do, but we also recognize the risks and don't compromise our coding standards. The hiring process: After a call with our Technical Recruiter, you'll do several technical Zoom calls with members of our engineering team: code screen, coding round, and system design round. If those go well we'll invite you to our NYC HQ for a final round interview. You'll meet one of our co-founders, someone from outside engineering, and meet more people from Radar. We'll go into more depth about
