Senior Machine Learning Engineer, Moloco NEXT

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?

• 5+ years of machine learning experience with a track record of shipping production-grade models in business-critical environments. We don't filter on degrees.
• Experience with data analysis
• Experience working on large-scale prediction or decisioning systems — CTR/CVR, ranking, recommendation, personalization, or related.
• Experience writing code in Python
• Comfortable functioning under ambiguity
• Bonus: experience using LLMs as agents or feature extractors — especially in evaluation, experiment debugging, or signal-discovery contexts.