This listing pays about $215,000 a year. Across 696 open software engineer listings that state pay, the middle half runs $168,000 to $251,000 and the median is $201,500. That is inside the middle half of those listings, 7% above the median.

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How AI is used in this kind of work

Among software developers, AI is already used for some of the tasks. Measured exposure for software developers: 29% (some).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

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