About the role
We're looking for Product Engineers to build machine learning-based systems into core Radar products. With steady-state API traffic of 10,000 qps, Radar is a scaled, data-intensive application where machine learning improves quality and accuracy. 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 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.
What you’ll do:
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Work on core Radar ML infrastructure built with Python, Rust, Airflow, Spark and new systems you build
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Build new systems for our Fraud products: anomaly detection, user and device risk scores, device fingerprinting, and emerging threat vectors
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Build new systems for our Maps products: search ranking and query classification for addresses, points of interest and others leveraging "learn-to-rank" systems, LightGBM, and new systems you build. Create road traffic models based on historical and live data to improve the ETA accuracy of our routing engine. Leverage AI to ingest address and point-of-interest data from across the web.
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Build new systems for our Geofencing products: indoor positioning leveraging mobile device sensors (BLE, barometer, motion, ultra-wideband, etc),
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Work on full-stack features across several of backend, data infra, mobile, and frontend
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Push the limits of what is possible with location services on iOS and Android
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Have your work run on 300M+ devices
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Talk to Radar customers and prospects, hear their feedback, incorporate it into your work, and make them successful
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. All projects are run by an Engineering lead, an executive, and built alongside go-to-market roles (sales engineering, customer success, sales, etc). Engineers figure out what to build, talk to customers, talk to prospects, help close them, get them live and make them successful.
One of our most important company values is "Walk a mile," as in walking a mile in the customer's shoes, but also as in literally walking a mile, getting up from your desk and developing a deep understanding of how location services behave in the real world. 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 call 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.
The hiring process:
After a brief call with our CTO, we'll invite you to our NYC HQ for an interview if we think there is a possible match. During that time we'll work on system design problems together and do some coding for 2 hours to build something simple. You might meet one of our co-founders and we'll also go into more depth about how we work to see if there is a match.
You should:
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Have experience building machine learning based products
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Don't think of yourself as an "ML Engineer"
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Are interested in talking to customers or prospects and making them successful
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Are deeply curious about how things work, and have the tenacity to sit with hard problems and power through them
Bonus points if you:
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Are a former technical co-founder
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Have experience with anomaly detection, indoor positioning, or mapping infrastructure
You’ll work with:
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Nick Patrick, Co-Founder and CEO
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Tim Julien, CTO
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Our customers and prospects
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Our Customer Success, Sales Engineering, and Sales teams
Benefits & Perks:
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Competitive compensation package, stock option grants, and a 401(k) plan with 4% employer match
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Medical, dental, and vision plans with 100% premiums covered, 12 weeks of paid parental leave, flexible spending accounts (FSAs), and more
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Unlimited PTO
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Fully stocked kitchen and weekly catered breakfast/lunch at our NYC office
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CitiBike and Wellhub (Gympass) memberships
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Monthly happy hours and team events
Benefits offering details will be provided if a candidate receives an offer. Benefits may also vary by location.
About Us
Radar is location infrastructure for every product and service. Companies like Vercel, Panera, and T-Mobile use Radar's geofencing SDKs and maps APIs to power location-based experiences across hundreds of millions of devices worldwide.
Founded in 2016, Radar is headquartered in New York, NY. Radar has raised $85.5M from leading venture capital firms including Accel and Insight Partners.
Compensation:
For candidates based in the United States, the base salary range for this full-time position is between $150,000 - $250,000/year with an opportunity for performance bonuses and incentives.
In addition to cash compensation, Radar offers full-time employees stock option grants under its equity plan. This is a meaningful ownership stake in the company we provide to our employees as we build a category-defining company.
Our salary ranges are determined by role, level, and location. The range displayed on this job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Your exact offer may vary based on market location, job-related skills, experience, and relevant education or training.
We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace.
Compensation Range: $150K - $250K
