Senior Machine Learning Systems Engineer, Ads ML Experience Platform

1d•$216,700 - $303,400•Remote

About The Position

Reddit is building the next generation of ML research tools and agentic AI platforms that power machine learning development across Reddit. The mission is to accelerate the Ads ML lifecycle – from experimentation and training to deployment, evaluation, and autonomous operations – through scalable platform services, intelligent automation, and developer-centric tooling. The team owns critical platform capabilities including offline ML experimentation systems, production training orchestration frameworks, ML lifecycle automation and, agentic ML frameworks that enable faster model iterations. They are looking for an experienced engineer with deep expertise in large-scale distributed systems, ML platforms, and emerging agentic architectures to help define and build the foundational tooling for the next generation of their machine learning devX tooling.

Requirements

5+ years in infrastructure/platform engineering or large-scale distributed systems.
2+ years of hands-on experience building and operating production ML infrastructure, developer SDKs, platform APIs, or self-service AI tooling.
Experience building workflow orchestration systems, developer platforms, or large-scale automation frameworks.
Experience with distributed data processing systems such as Spark, Flink, Ray, or equivalent technologies.
Experience with modern orchestration and workflow technologies such as Kubeflow, Argo, Airflow, or similar frameworks.
Experience building offline ML experimentation platforms, model registries, experiment tracking systems, or training orchestration frameworks.

Nice To Haves

Experience building and operating agentic AI systems, including multi-agent orchestration, autonomous workflows, and agent communication/runtime frameworks (e.g., MCP, A2A, and orchestration systems) is a strong plus
Experience running end-to-end model development and iteration cycles at scale is a plus

Responsibilities

Design and build large-scale offline ML experimentation platforms that enable reproducible research, model development, evaluation, and promotion workflows.
Develop production-grade training orchestration frameworks supporting distributed training, hyperparameter optimization, model evaluation, and automated retraining.
Build infrastructure for experiment tracking, metadata management, lineage, artifact versioning, model registries, and reproducibility.
Partner with ML engineers and researchers to improve experimentation velocity and operational efficiency.
Build automated workflows for model promotion, rollback, compliance validation, and continuous evaluation.
Design and build an agentic AI execution platform supporting autonomous and human-in-the-loop workflows, including multi-agent orchestration, memory/context systems, and scalable workflow infrastructure.