Builds ML-based systems integrated into Radar's geofencing, maps, and fraud products across backend, data infra, and mobile SDKs. Drives end-to-end features leveraging ML for production-scale impact, collaborates with customers.
200k – 300k/yr
On-siteML Engineering
About the role
Responsibilities
Work on core Radar ML infrastructure built with Python, Rust, Airflow, Spark and new systems.
Build new systems for Fraud products: anomaly detection, user and device risk scores, device fingerprinting, and emerging threat vectors.
Build new systems for Maps products: search ranking and query classification for addresses, points of interest leveraging "learn-to-rank" systems, LightGBM; create road traffic models to improve ETA accuracy; leverage AI to ingest address and POI data.
Build new systems for Geofencing products: indoor positioning leveraging mobile device sensors (BLE, barometer, motion, Ultra-Wideband).
Work on full-stack features across backend, data infra, mobile, and frontend.
Push limits of location services on iOS and Android.
Talk to customers and prospects, incorporate feedback.
Requirements
Experience building machine learning based products in production at scale.
Don't think of yourself as an "ML Engineer".
Interested in talking to customers or prospects and making them successful.
Deeply curious about how things work, tenacity to power through hard problems.
Nice-to-Haves
Former technical co-founder.
Experience with anomaly detection, indoor positioning, or mapping infrastructure.
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