Job description

About Radar Radar radar.com 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 anti-fraud systems into core Radar products. The ideal engineer for this role is someone who has built ML based fraud detection models, leveraged in a risk based rules engine. 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 to prevent fraud. 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. 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 how we work to see if there is a match. What you’ll do: - Work on core Radar ML infrastructure built with Python, Rust, Airflow, Spark and new systems you build - Build new systems for our Fraud products: anomaly detection, user and device risk scores, device fingerprinting, and emerging threat vectors - Work on features across several of backend, data infra and ML - Push the limits of fraud detection using many sensors on iOS and Android - Have your work run on 300M+ devices - Talk to Radar customers and prospects, hear their feedback, incorporate it into your work, and make them successful You should: - Have experience building machine learning-based fraud detection products in production at scale - Are interested in talking to customers or prospects and making them successful - Are deeply curious about how things work, and have the tenacity to sit with hard problems and power through them Bonus points if you: - Are a former technical co-founder - Have experience with anomaly detection, anti-fraud ML systems You’ll work with: - Nick Patrick linkedin.com, Co-Founder and CEO - Tim Julien linkedin.com, CTO - David Gurevich linkedin.com, Engineer - Our customers and prospects - Our Customer Success, Sales Engineering, and Sales teams What we offer: - Competitive salary - Meaningful stock options in a fast-growing company - 401(k) plan with 4% match - New HQ in Flatiron, NYC - Top-notch equipment - Catered lunches - Unlimited PTO - Health, dental, and vision insurance with 100% coverage for employees - 12 weeks of paid parental leave - Commuter and fitness benefits We’ll share full details of our benefits package at the offer stage. Benefits may vary by location. Radar is an equal opportunity employer and does not discriminate on the basis of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, veteran status, or any other characteristic protected by law.

Requirements

Must-haves

To us, a week is a long time, and we expect to ship big things every week.
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.
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.
Work on core Radar ML infrastructure built with Python, Rust, Airflow, Spark and new systems you build
Build new systems for our Fraud products: anomaly detection, user and device risk scores, device fingerprinting, and emerging threat vectors
Work on features across several of backend, data infra and ML
Push the limits of fraud detection using many sensors on iOS and Android
Have your work run on 300M+ devices
Talk to Radar customers and prospects, hear their feedback, incorporate it into your work, and make them successful
Have experience building machine learning-based fraud detection products in production at scale
Are interested in talking to customers or prospects and making them successful
Are deeply curious about how things work, and have the tenacity to sit with hard problems and power through them
Are a former technical co-founder
Have experience with anomaly detection, anti-fraud ML systems
Our customers and prospects
Our Customer Success, Sales Engineering, and Sales teams

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapimachine learningmlrusttypescriptnode.jsawsgopython

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave