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New York, New York, United States
Onsite
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 a Maps Product Engineer to strengthen the data foundation behind Radar's geocoding platform. Our daily traffic is over 1 Billion API calls / day (15,000 requests / second) so you will be operating at scale. You'll own the systems that bring many sources of location data together into something reliable, accurate, and fresh, with the opportunity to bring AI into how we enrich and scale it. It's a broad role with deep ownership of the data layer, reaching across the geocoding stack from ingestion to serving. This role will be in our NYC HQ.
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:
Our core backend service is HorizonDB, a geospatial database written in Rust that powers geocoding (see our blog posts here and here for a look at the internals)
We use Tantivy, RocksDB, and FSTs for indexing, with ML models for query understanding and ranking.
Our geospatial data pipelines are written in Scala and Python on Airflow, bringing many commercial and open data sources together.
The API layer is a Node.js TypeScript app, and we use MongoDB, S3/Athena, and Redis.
Everything is deployed to AWS via Kubernetes on EKS using Terraform.
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 how we work to see if there is a match.
What you’ll do:
Own the data foundation under the geocoder including how location data is modeled, kept consistent, and served reliably, working across Scala, Python, and Rust
Build the pipelines that bring many commercial and open data sources together, expanding coverage and quality across US and international data
Help bring LLMs and agents into the data layer for enrichment, labeling, and quality, with the guardrails to keep them trustworthy
Range across the geocoding stack when the work calls for it, from the serving path in HorizonDB to the Node.js/TypeScript API
Have your work power over 1 Billion API calls / day
Talk to Radar customers and prospects, hear their feedback, incorporate it into your work and make them successful
You should:
Think of yourself as a generalist engineer first, not tied to any particular stack
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 worked on a high throughput, high growth SaaS application
Have worked on geospatial infrastructure
Have built large-scale data pipelines or data-integration systems
Have used LLMs in a data or ML pipeline (enrichment, labeling, or evaluation)
You’ll work with:
Tim Julien, CTO
Rishant Dwivedi, Engineering Manager
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.