Software Engineer - Backend

Department: Engineering

Location: San Francisco, New York

Compensation: $180K – $250K • Offers Equity

Employment Type: FullTime

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 of the marketplace.

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

• Build scalable data models that support multiple impressions for an ad request allowing us to align publisher and advertiser interests

• Design abstractions that allow us to experiment and ship new formats that allow for a flexible way of tracking engagement events

• Build a low-latency frequency capping system to improve an end user's experience and prevent ad fatigue

• Create schemas to represent high-volume ad bidding with ad exchanges to increase the diversity of the ad demand on platform

• Evolve our real-time auction and ad-decisioning systems to improve publisher, advertiser, and user outcomes

You might be a fit if

• You like debugging and optimizing performance

• You are detail oriented and like to create good abstractions that others rely on

• You enjoy experimentation, measuring, and creating reliable systems

• You want ownership of foundational systems

• You enjoy working closely with product and data science to turn ambiguous problems into measurable outcomes