Moloco
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42 Moloco Machine Learning Engineer Jobs Hiring Near You

Senior Applied Scientist - Moloco Ads

Seattle, WA · On-site +1

$104K - $142K/yr

... infrastructure engineering teams, machine learning teams and data science teams. As an Applied ... sure the Moloco system is running safely and efficiently. * Learn from your senior peers how to ...

Built with AI from day one, Moloco's planet-scale machine learning platform powers a suite of ... As a Senior Software Engineer, you will design, develop, and optimize distributed data systems for ...

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Senior Machine Learning Engineer, Moloco NEXT

Moloco

Menlo Park, CA • On-site

$123K - $169K/yr

Full-time

Posted 11 days ago


Job description

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.