> Markdown version of [/jobs/ext/1891155-machine-learning-engineer-moloco-next](https://www.wearedevelopers.com/jobs/ext/1891155-machine-learning-engineer-moloco-next). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer, Moloco NEXT - **Company:** MOM COMMERCE MEDIA LLC - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $200,000.0 - $260,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Software Debugging, Python (Programming Language), Machine Learning, Large Language Models, Virtual Agents, Software Coding - **Published:** August 2, 2026 - **Apply:** https://www.dice.com/job-detail/74da3aaa-71eb-4510-8a47-802fe1895f0b ## About the Role * 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. ## Description 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., * Lead with Humility: Everyone's voice is respected, valued, and heard. With humility, we become more open and accessible to each other. We win, lose, and learn together. Accountability and feedback are essential to our success. * Uncapped Growth Mindset: We see all situations as opportunities to learn, grow, and improve as individuals and as an organization. We seek diverse perspectives, encourage curiosity, and promote experimentation to push the boundaries of what's possible. * Create Real Value: We pursue the most impactful opportunities with rigor and integrity. We take intelligent risks and make disciplined trade-offs to maintain deep focus. We help our customers win by delivering durable value. * Go Further Together: We're one team working towards one mission and vision. We collaborate proactively and inclusively, involving the right people at the right time and in the right way. We strive to create a more equitable workplace. We won't let each other fail. Additional Resources: * Moloco Company Blog * Moloco Leadership * Moloco Newsroom AI Use in Interviews Our interview process is designed to get to know the real you. Unless a round specifically includes AI as part of what's being assessed, we ask that candidates engage without AI assistance. Please review our AI Use in Interviews Policy before your interview to understand what to expect. Failure to comply with this policy may impact your candidacy. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [MLOps - What’s the deal behind it?](https://www.wearedevelopers.com/videos/392-mlops-what-s-the-deal-behind-it) - [Guiding Agentic AI with Vue](https://www.wearedevelopers.com/videos/2033-guiding-agentic-ai-with-vue) - [Unleashing the Power of Developers: Why Cybersecurity is the Missing Piece?!?](https://www.wearedevelopers.com/videos/712-unleashing-the-power-of-developers-why-cybersecurity-is-the-missing-piece) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)