Senior Machine Learning Systems Engineer, Ads ML Experience Platform

About this role

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .

Reddit has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence Team Overview We are building the next generation of ML research tools and agentic AI platforms that power machine learning development across Reddit.

Our 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. Our 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.

We 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 our machine learning devX tooling. What You’ll Do 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.