Senior Machine Learning Systems Engineer

Who you are

• 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
• 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

What the job involves

• 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
• 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

Benefits

• Comprehensive health benefits
• Flexible vacation & Reddit global days off
• Family planning funds & 4+ months paid parental leave
• Personal & professional development funds
• Paid volunteer time off
• Workspace & home office benefits