Senior Machine Learning Engineer, Moloco NEXT

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!

The Impact You’ll Be Contributing to Moloco:

Moloco NEXT is Moloco's performance advertising platform within Moloco. As a Senior Machine Learning Engineer on NEXT, you'll own the CTR/CVR prediction models inside a real-time bidding system that decides on every ad request in under 100ms.

The Opportunity:

• Own a production CTR/CVR prediction model end-to-end — modeling, eval, feature pipelines, online experimentation, and post-launch ops. By month six, you'll own a meaningful slice of NEXT's ML stack.
• Hunt for the missing signals that move the needle: new data sources to log and ingest, derived and contextual features the current model doesn't yet see. On NEXT, most wins come from finding signals others missed — not from architectural cleverness.
• Run the loop fast: design offline evaluation, ship to online A/B, read out in days, iterate. Diagnose the offline-online divergences when they show up — and they will.
• Build the agentic tooling that automates parts of our experiment-debugging and signal-discovery workflow, both as a contributor and as a user.
• Set technical direction. Decide what NEXT should bet on next quarter, not just execute on assignments. Bridge to the data and pipeline teams whose signals feed our models — most signal-hunting wins depend on getting those teams aligned.
• Embrace the unglamorous parts: data-quality instrumentation, train/serve consistency in feature pipelines, slicing eval to find failure modes, and the careful experiment debugging that separates real wins from noise.

How Do I Know if the Role is Right For Me?