Applied Scientist
At a glance
Mid-level AI Research role at Koah. San Francisco · full-time · $200,000–$300,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build real-time bidding algorithms, drawing on existing research about pricing
- Develop regression models that predict conversions from demographic, audience and semantic signals, then put them into production
- Use privacy-preserving clustering to group conversational data and improve advertiser outreach
- Analyze data for improvements to ad matching while balancing publisher and advertiser outcomes
What you bring
- An advanced degree in physics, computer science, mathematics or another related field
- Strong ability in mathematical and statistical modeling
Who this fits
This role suits you if you like taking on difficult research questions, testing hypotheses and learning continuously. You’ll need to explain your findings to both technical and non-technical audiences. The role is hybrid in San Francisco.
From the employer
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