Unknown Company

Applied Scientist

san francisco, ca • Posted 1 weeks ago
Onsite Full Time Other

Who We Are


Koah Labs is building the ad network to power the next generation of AI-native products. Our mission is to help publishers monetize and help advertisers reach the right audience — without compromising speed, UX, or privacy.


We’re a small, tight-knit team in San Francisco with backgrounds at X, Apple, Meta, and early-stage startups. We’ve raised from top investors and are growing fast with real traction on both the publisher and advertiser sides.


Working at Koah means joining at the ground floor: you’ll ship code that shapes the company and the ecosystem we’re building. We move quickly, operate with high trust, and care deeply about craft.



Our Stack



  • Infra : Terraform, AWS, LGTM (Loki, Grafana, Tempo, Mimir), Tailscale, Cloudflare


  • Data : PostgreSQL, ClickHouse, Redis, Kafka, Python


  • Core Application : Ruby on Rails, React, TypeScript


  • SDKs : Flutter, React Native, Android, iOS



Example Projects



  • Design efficient algorithms for real-time bidding systems, building upon the current pricing literature


  • Create and productionize regression models to predict end conversions based on demographic, audience, and semantic data


  • Apply privacy-preserving clustering methods to categorize conversational data to improve advertiser outreach


  • Analyze and pore over data to find alpha that can improve the core ad matching system balancing publisher and advertiser outcomes



You might be a fit if



  • You have an advanced degree in Physics, Computer Science, Mathematics, Statistics, Engineering, or a related field


  • You enjoy identifying and owning challenging problems, forming testable hypotheses, and conducting impactful research to drive significant business impact


  • You have a relentless focus on continuous learning and making an impact with an ability to question the status quo


  • You have strong mathematical and statistical modeling skills


  • You enjoy communicating conclusions to both technical and non-technical audiences alike


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