Expression of Interest: Machine Learning Engineer

Moloco•Seattle, WA

About The Position

As a Machine Learning Engineer at Moloco, you will design, train, and deploy the large‑scale models that power our programmatic advertising and commerce media products. You will work at the heart of our real-time bidding and pricing systems, helping shape an end-to-end ML ecosystem that processes billions of daily events. Your work will directly improve marketplace performance for global advertisers and publishers by optimizing for relevance, ROI, and user experience at a scale few companies can match.

Requirements

Production ML at Scale: 5-12+ years of experience delivering ML solutions in high-stakes production environments (e.g., Ads, Recommenders, Search, or Marketplaces), with a track record of scaling models for massive throughput and low-latency requirements.
Algorithmic Mastery: A strong foundation in ML and statistics applied to real-world business challenges, with expertise in areas such as ranking, calibration, exploration-exploitation, causal inference, or multi‑objective optimization.
Full-Lifecycle Engineering: Deep understanding of the end-to-end ML lifecycle, including data pipelining, feature engineering, model architecture, inference optimization, and the deployment of models into online systems.
Technical Versatility: Proficiency across a modern ML toolkit (Python, SQL, and frameworks like TensorFlow or PyTorch) and experience leveraging data processing stacks like Spark, Beam, or Flink to deliver results at scale.
Operational Stewardship: Experience ensuring the long-term health of ML systems in live environments—monitoring performance, diagnosing drift, and iterating based on real-world feedback to maintain measurable impact.
Technical Mentorship: A passion for advancing the team’s collective bar by sharing best practices for large-scale ML systems and guiding the technical direction of complex machine learning initiatives.
Your ethos is ownership
You thrive in ambiguity and enjoy partnering with others to shape vague requirements into clear roadmaps with pragmatic tradeoffs.
You take shared accountability for the end-to-end journey; communicating progress, surfacing risks early, and following through to ensure your work creates measurable value.
You demonstrate strong judgment under uncertainty.
You prioritize based on long-term impact, over short-term noise, making deliberate decisions when information is incomplete.
You explicitly weigh tradeoffs like speed vs. quality and remain agile enough to adjust your approach thoughtfully as new information/data emerges.
You lead through technical clarity and active listening.
You reduce ambiguity for the team by asking the right questions upfront, tailor your communication to your audience, document your decisions, and bring helpful structure to complex discussions.
You provide thoughtful, evidence-based feedback when needed and align behind decisions once they are made.
You build consensus through collaboration, not ego.
You build trust across the organization by listening first and navigate conflicting needs with empathy, creating buy-in around priorities and scope.
You navigate technical tensions productively, disagreeing thoughtfully while maintaining momentum and strengthening professional relationships.
You think in systems, not silos.
You look beyond immediate tasks to understand root causes, second-order effects, and the long-term health of our ML ecosystem.
When something breaks, you don’t just patch it; you seek structural improvements that prevent recurrence and elevate the technical bar for the entire organization.
You prioritize customer impact and take action to make it real.
You prioritize effective solutions that drive business impact over technical elegance, spotting opportunities that others might overlook.
You have the initiative to turn ideas into outcomes and the agency to identify and fix gaps, even when they aren’t yet on the formal roadmap.

Responsibilities

Architect and iterate on high-performance models that improve ad relevance, click-through rates, and conversion performance.
Productionize and maintain scalable ML pipelines - from data ingestion to online inference - on top of planet-scale infrastructure.
Extract insights from massive datasets of user behavior and auction signals to define new features and refine modeling strategies.
Translate business goals (revenue, ROI, engagement) into concrete modeling challenges and success metrics in collaboration with Product and Data Science.
Validate innovation through rigorous experimentation, including A/B tests, offline evaluations, and counterfactual analyses.
Balance marketplace health by implementing models that harmonize advertiser value with a high-quality user experience.
Enhance system reliability by refining feature stores, monitoring data quality, and establishing robust debugging practices.
Drive platform evolution, contributing to ML tools and guidelines that increase experimentation velocity and deployment speed.
Optimize for performance, partnering with Infra teams to reduce latency and improve throughput for low-cost, high-scale decisioning.
Elevate technical excellence by sharing best practices in modeling and production ML with the broader engineering team.