Senior ML Systems Engineer, Ads Platform & Agentic AI

Reddit, Inc.; New York (NY); On-site; USD 217,000 - 303,000; Full time

Benefits offered by this job

Comprehensive Healthcare Benefits and

401k with Employer Match

Global Benefit programs that fit your…

Job summary

Reddit, Inc. is seeking an experienced engineer to design and operate large-scale ML infrastructure and agentic AI tooling. You will build offline experimentation platforms, training orchestration, and model registries to accelerate ML research and deployment.

You will collaborate with ML researchers to boost experimentation velocity, implement multi-agent orchestration, and enable autonomous workflows across distributed systems at scale.

Qualifications

• 5+ years in infrastructure/platform engineering or large-scale distributed systems.
• 2+ years building/operating production ML infrastructure or developer tooling.
• Experience with workflow orchestration systems or large-scale automation frameworks.
• Experience with distributed data processing systems like Spark, Flink, or Ray.
• Experience with Kubeflow, Argo, Airflow or similar frameworks.

Responsibilities

• Design and build large-scale offline ML experimentation platforms enabling reproducible research and model development.
• Develop production-grade training orchestration frameworks for distributed training and hyperparameter optimization.
• Build infrastructure for experiment tracking, metadata management, lineage, and model registries.
• Partner with ML engineers to improve experimentation velocity and operational efficiency.
• Create agentic AI execution platform supporting autonomous and human-in-the-loop workflows.

Skills

Distributed systems

ML infrastructure

Workflow orchestration

Experimentation platforms

Autonomous workflows

Tools

Spark

Flink

Ray

Kubeflow

Argo

Airflow

MCP

A2A

Job description

Reddit, Inc. is seeking an experienced engineer to design and operate large-scale ML infrastructure and agentic AI tooling. You will build offline experimentation platforms, training orchestration, and model registries to accelerate ML research and deployment.

You will collaborate with ML researchers to boost experimentation velocity, implement multi-agent orchestration, and enable autonomous workflows across distributed systems at scale.