Senior/Staff Software Engineer, Search
Radar · New York ; US Remote
About this role
## About Radar Radar (https://radar.com) is the global leader in geolocation, with geofencing SDKs, maps APIs, and AI-enabled solutions for marketing, fraud, and operations teams. **Why Radar is a great place to work:** - Trusted by top companies from high-growth startups to the Fortune 500 - Processing **1B+ API calls/day** from hundreds of millions of devices - Raised **$85.5M** from investors including Accel and Insight Partners - High-performance, ambitious, entrepreneurial culture - New office in **Flatiron, Manhattan (NYC)**; named a **top 10 best place to work in NYC** by Crain’s --- ## Role: Senior/Staff Software Engineer, Search Radar is looking for a **Senior/Staff Software Engineer (Search)** to improve the data and search systems behind Radar’s geocoding platform. You’ll operate at scale (daily traffic **1B+ API calls/day**, ~**15,000 requests/second**) and help: - Improve **location data quality and coverage** - Build and automate **pipelines** that keep data growing - Enhance **query understanding**, **candidate retrieval**, and **ranking** across **geocoding** and **autocomplete** - Use **ML and LLMs** where they materially improve systems (e.g., enrichment, matching, relevance) This is a **software engineering role first**—you don’t need a research background, but you should be excited to work across **data, search, and backend systems**, and use ML when it’s the right tool. **Location:** NYC HQ --- ## How you’ll work - Engineering culture emphasizes **shipping fast** and **talking to customers** - Weekly planning on **Mondays** (small groups) using **Linear** - Most engineers are former technical co-founders or Radar interns (e.g., Waterloo, CMU) - Engineers typically fit one of two molds: - **Staff-level expertise** in one stack - **Multi-Stack** (learning across stacks like Mobile or Data, not just frontend/backend) --- ## Tech stack (high level) - **HorizonDB**: geospatial database written in **Rust** powering geocoding (forward/reverse), autocomplete, and address validation - Custom search indexes and storage (e.g., **Tantivy**, **RocksDB**, **FSTs**) - ML models for query understanding and ranking - Data pipelines: **Scala/Python** on **Airflow** (merging commercial + open data) - API layer: **Node.js + TypeScript** - Data/services: **MongoDB**, **S3/Athena**, **Redis** - Deployment: **AWS + Kubernetes (EKS)** with **Terraform** - On-call rotation: most engineers participate --- ## How Radar uses AI - Engineers choose AI tools (e.g., **Claude**, **Codex**) - Actively building Claude skills (e.g., taught it to debug HorizonDB) - Code changes and PRs are reviewed by engineers knowledgeable in the area - AI is used with care—**no compromise on coding standards** --- ## What you’ll do - Improve **geocoding and autocomplete quality** via query understanding, retrieval, ranking, and relevance - Use **ML/LLMs** to enrich, label, match, and expand location data at scale - Automate **data pipelines** to grow coverage/detail and reduce manual work - Onboard new/messy data sources; improve normalization, matching, and integration (US + international) - Work across the boundary between **data and search** (indexing, retrieval, ranking, surfacing) - Contribute across the stack (Scala/Python pipelines to HorizonDB search/serving in Rust) - Power systems used by **1B+ API calls/day** - Talk to customers/prospects, incorporate feedback, and make them successful --- ## You should - Be a **generalist engineer
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