Applied Scientist
Posted 1 Oct 2025
Advertised as “Senior/Staff Applied Scientist (시니어/스태프 응용 과학자)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 2+ yrs
- Management level
- Individual contributor
No salary listed for this role.
CertificationsPh.D.
Field of studyComputer Science, Economics, Operations Research, Statistics (or related)
About Moloco:
Moloco is an AI-native performance advertising company, bringing the power of AI advertising to the open internet. Founded in 2013 by machine learning engineers who had built AI advertising systems inside the world's largest technology companies, Moloco began from a shared mission: to build that same engine for the open internet.
Today, Moloco is one company with two businesses, built on the same compound AI system. Moloco Ads is our AI-native performance advertising platform, helping mobile app marketers acquire, retain, and re-engage high-value users across the open internet. Moloco Commerce Media is our AI-native retail media platform, enabling retailers and marketplaces to turn their platforms into thriving ad businesses. Both are powered by CARA (Compound Ad Recommendation Architecture), the AI system behind every Moloco product.
At Moloco, we bring together those who've already solved hard problems and those with the drive to. Here, we work on some of the complex problems in AI advertising, at a scale and level of sophistication very few companies can offer. Come build what's next.
Please submit both your resume and cover letter.
Why this role exists:
- Drive ambiguous signals to defensible root cause. A KPI regression. A lift number that looks too good. An unexplained cost increase. A model that quietly degraded after an upstream data change. You will trace it across the model, the data pipeline, the auction, and the serving stack — and be right.
- Ship the fix. A new feature, a reformulated objective, a recalibrated model, a changed bidding policy, or a correction upstream in the data. You own it through launch and through the readout afterward.
- Design evaluation that survives scrutiny. Online experiments and offline evaluation in a setting where auctions are non-stationary, treatment and control interfere with each other, and conversions land days late.
- Leave behind methodology, not just results. Tooling and frameworks that let other teams answer the next version of the question without you.
- Communicate to people who will act on it. ML engineers, infrastructure, product, and the account teams sitting across from advertisers.