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.

What we're looking for?

Required

Ph.D. in computer science, statistics, operations research, economics, or a related quantitative field, plus 2+ years of industry experience — or 5+ years of industry experience solving large-scale ML, optimization, or systems problems without one.
Strong applied statistics and causal reasoning: experiment design, working with confounded observational data, and the judgment to recognize when a result is not real.
Fluency in Python and SQL against large datasets (Spark, BigQuery, or equivalent). You can get your own data without waiting on anyone.
Experience owning a model or algorithm in production — including what happened to it after launch.
Clear written English. A large share of this role's impact takes the form of a document that changes what a team decides to do.

Strongly Preferred - any of

Ads, recommendation, search ranking, marketplaces, or another domain with real-time auctions or bidding.
Probability calibration, delayed feedback, or selection bias in logged-bandit data.
CTV, attribution modeling, or incrementality measurement.
PyTorch or TensorFlow applied to tabular or sequential production data at scale.

Senior vs. Staff

Both are individual-contributor roles. Level is decided at offer, based on demonstrated scope rather than years served.

Senior — in the last 12–18 months you have

Taken a project from an ambiguous problem statement to a shipped, measured change in production.
Made the calls yourself on what to investigate and, just as importantly, what to drop.
Made at least one teammate measurably better through review, pairing, or mentorship

Staff — all of the above, plus

Led work that spanned multiple teams or systems, where no single team owned the problem.
Changed what your organization chose to work on, on the strength of analysis you produced.
Left behind a method, framework, or standard that other people now use by default.