Machine Learning Engineer - Inference / Serving
As a Machine Learning Engineer focusing on Inference and Serving, you will design, optimize, and operate the systems that deploy Yobi's Behavioral AI models in real time. This applied ML systems role involves ensuring performant, reliable, and continuously improving services for open-web and CTV products, with responsibilities spanning model deployment, optimization for low-latency inference, and operational maturity.
Yobi is a rapidly growing Behavioral AI company on a mission to ethically democratize the benefits of data and AI. Since 2019, we have built one of the largest consented behavioral datasets in the United States, extending far beyond the walled gardens of Big Tech. Unlike traditional LLM companies, Yobi builds foundation models of human behavior grounded in real-world actions such as purchases and store visits. Our private-by-design modeling enables state-of-the-art personalization and decisioning for leading brands and agencies while protecting privacy, safety, and ethics. Today, we are focused on bringing the performance of closed-web user acquisition to the open web and connected TV, giving brands walled-garden results without the walls. At our core, Yobi is building the behavioral intelligence layer for any system that makes a personalization decision.
We’re at an inflection point—customer adoption is accelerating, but there’s still room to shape the architecture and culture from the ground up. Engineers here own major surface areas, build 0→1 systems in large-scale data and model infrastructure, and help define how Behavioral AI scales ethically and effectively.
As a Machine Learning Engineer focused on Inference and Serving at Yobi, you’ll design, optimize, and operate the systems that bring our Behavioral AI models to life in real time. You’ll work at the core of our production environment, turning trained models into performant, reliable, and continuously improving services that power our open-web and CTV products. This is an applied ML systems role—equal parts engineering depth, deployment craft, and model intuition. You’ll shape how models are packaged, versioned, rolled out, and observed across environments, ensuring every prediction is fast, accurate, and accountable.
Posted June 8, 2026