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About Moloco:
Moloco builds some of the most powerful AI advertising solutions in the world. Our name—short for "machine learning company"—reflects our core mission: democratizing access to the advanced AI that has historically been reserved for tech giants. Led by machine learning pioneers who built some of the most successful ad systems at Google, including YouTube's monetization engine and key search advertising technologies, we're transforming how businesses grow and compete in the digital economy.
Built with AI from day one, Moloco’s planet-scale machine learning platform powers a suite of solutions for advertising growth and monetization. Moloco Ads is an AI-powered platform that delivers real business outcomes for mobile app marketers through performance-based user acquisition. Moloco Commerce Media enables retailers and marketplaces to build revenue-generating ad businesses that balance user experience and advertiser performance.
Moloco is headquartered in Silicon Valley, with offices in Seattle, New York, San Francisco, Seoul, Beijing, Singapore, Gurgaon, Tokyo, Shanghai, London, Tel Aviv, and Berlin.
Moloco is a truly rewarding place to work and in an exciting period of growth, which you could be a part of. Join us today and apply now!
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.
· Represented technical trade-offs directly to non-technical stakeholders, and been trusted to do it.
This role is probably not for you if you are looking for a purely offline research position, or you prefer to hand a model to an engineer and move on to the next paper.
Moloco Thrive: Benefits and Well-Being:
We take care of you and create the conditions for you to do the best work of your career. Through a lens of inclusion, we offer innovative benefits that empower our employees to take care of themselves and their families so they can do the best work of their lives.
Moloco Values:
· Lead with Humility: Everyone’s voice is respected, valued, and heard. With humility, we become more open and accessible to each other. We win, lose, and learn together. Ac