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At Koah Labs, we believe in rewarding world-class talent with highly competitive compensation. Our approach ensures that every team member is recognized and incentivized for their impact with a comprehensive package that includes a strong base salary, meaningful equity, and top-tier benefits. We are committed to providing an environment where our people are empowered to do their best work, knowing their skills and contributions are valued and rewarded accordingly. 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 $5.7M 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.
Job Responsibility:
Sit within product engineering and help drive product decisions using data and causal reasoning
Design, implement, execute experiments and analyze results
Help level-up all of engineering, encouraging data driven decisions and a deep understanding of the important metrics that drive our business forward
Use rigorous statistical thinking and hands-on modeling to turn our rich marketplace data into tools that directly shape product decisions and key insights
Requirements:
Advanced degree in Physics, Computer Science, Mathematics, Statistics, Engineering, or a related field
Enjoy identifying and owning challenging problems, forming testable hypotheses, and conducting impactful research to drive significant business impact
Relentless focus on continuous learning and making an impact with an ability to question the status quo
Strong mathematical and statistical modeling skills
Enjoy communicating conclusions to both technical and non-technical audiences alike