Expression of Interest: Machine Learning Engineer
at Moloco
- Location
- Menlo Park, California, United States; New York, New York, United States; Seattle, Washington, United States
- Posted
- 12d ago
pythongoc++awsgcpkubernetessparktensorflowpytorch
at Moloco
<div class="content-intro"><p><strong>About Moloco:</strong></p> <p>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.</p> <p>Built with AI from day one, Moloco’s planet-scale machine learning platform powers a suite of solutions for advertising growth and monetization. <a href="https://www.moloco.com/solutions/moloco-ads"><strong>Moloco Ads</strong></a> is an AI-powered platform that delivers real business outcomes for mobile app marketers through performance-based user acquisition. <a href="https://www.moloco.com/solutions/moloco-commerce-media"><strong>Moloco Commerce Media</strong></a> enables retailers and marketplaces to build revenue-generating ad businesses that balance user experience and advertiser performance.</p> <p>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.</p> <p>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!</p></div><p><strong>Expression of Interest</strong></p> <p>We know that for the most talented engineers, the right opportunity is about more than just a job title, it’s about the right challenge at the right time. Even if our current openings don't perfectly fit your timing and interests today, we still want to hear from you. We view this as a priority pathway to connect with curious, driven MLEs whose unique expertise can help us architect the next generation of planet-scale ML systems. </p> <p>At Moloco, our mission is to empower the global digital economy by turning massive first-party data into measurable business outcomes. Whether it’s optimizing real-time bidding for Moloco Ads or building AI-native stacks for Streaming and Commerce, we’re looking for engineers to help us lead the shift toward a more performance-driven, AI-powered internet.</p> <p>If you’re excited to scale and innovate in a fast-moving environment, share your profile and we will reach out the moment we see a potential match for your experiences and future goals.</p> <p><strong>About the Role</strong></p> <p>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.</p> <p><strong>The Opportunity</strong></p> <ul> <li><strong>Architect and iterate</strong> on high-performance models that improve ad relevance, click-through rates, and conversion performance.</li> <li><strong>Productionize and maintain</strong> scalable ML pipelines - from data ingestion to online inference - on top of planet-scale infrastructure.</li> <li><strong>Extract insights</strong> from massive datasets of user behavior and auction signals to define new features and refine modeling strategies.</li> <li><strong>Translate business goals</strong> (revenue, ROI, engagement) into concrete modeling challenges and success metrics in collaboration with Product and Data Science.</li> <li><strong>Validate innovation</strong> through rigorous experimentation, including A/B tests, offline evaluations, and counterfactual analyses.</li> <li><strong>Balance marketplace health</strong> by implementing models that harmonize advertiser value with a high-quality user experience.</li> <li><strong>Enhance system reliability</strong> by refining feature stores, monitoring data quality, and establishing robust debugging practices.</li> <li><strong>Drive platform evolution</strong>, contributing to ML tools and guidelines that increase experimentation velocity and deployment speed.</li> <li><strong>Optimize for performance</strong>, partnering with Infra teams to reduce latency and improve throughput for low-cost, high-scale decisioning.</li> <li><strong>Elevate technical excellence</strong> by sharing best practices in modeling and production ML with the broader engineering team.</li> </ul> <p><strong>Expertise We Value:</strong></p> <p>MLEs at Moloco are hands-on builders of some of the world’s highest-scale ML workloads. They bridge technical innovation with global revenue outcomes, owning the critical systems that define our next generation of machine learning and optimization platforms.</p> <p>While every journey is unique, we find that the most successful MLEs at Moloco bring a deep technical foundation in the following areas:</p> <ul> <li><strong>Production ML at Scale:</strong> 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.</li> <li><strong>Algorithmic Mastery:</strong> 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.</li> <li><strong>Full-Lifecycle Engineering:</strong> 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.</li> <li><strong>Technical Versatili