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Radar is the global leader in geolocation infrastructure, processing over 1 billion API calls per day. The company provides geofencing SDKs, maps APIs, and AI-enabled solutions for marketing, fraud, and operations teams, trusted by Fortune 500 companies and high-growth startups.
You will improve the data and search systems behind Radar's geocoding platform, operating at massive scale (15,000 requests/second). Your work will span geocoding quality, autocomplete, query understanding, retrieval, and ranking. You'll use ML and LLMs to enrich and match location data, automate data pipelines that keep coverage growing, and onboard new data sources while normalizing and integrating US and international location data.
The role requires working across the full stack: from data pipelines in Scala and Python (Airflow) to search and serving in HorizonDB (a custom geospatial database written in Rust). The core backend uses custom search indexes (Tantivy, RocksDB, FSTs), ML models for query understanding and ranking, and an API layer in Node.js/TypeScript. Infrastructure runs on AWS via Kubernetes/EKS with Terraform.
Radar's engineering culture emphasizes shipping fast, customer feedback, and in-person collaboration at their new Flatiron HQ. The team includes former technical co-founders and interns from top schools. Engineers are expected to be generalists comfortable jumping between stacks, and most participate in on-call rotation. The company uses Claude and Codex for code review and development, with all PRs reviewed by knowledgeable engineers.
You'll talk directly with customers and prospects, incorporate their feedback, and ensure your work powers billions of daily API calls. The company has raised $85.5M from Accel and Insight Partners, was named a top 10 best place to work in NYC by Crain's, and sponsors OpenStreetMaps, MapLibre, and OpenAddresses.
REQUIREMENTS:
- Think of yourself as a generalist engineer first, not tied to any particular stack
- Interested in talking to customers or prospects and making them successful
- Deeply curious about how things work with tenacity to solve hard problems
BONUS EXPERIENCE:
- Former technical co-founder
- High throughput, high-growth SaaS application experience
- Geospatial infrastructure background
- Applied ML or LLMs to real product or data problems
- Search, ranking, relevance, query understanding, or information retrieval systems experience
