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# Machine Learning Systems Engineer, ML Platform

- **Company:** reddit Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $216,700.0 - $303,400.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Content Analysis, Data Infrastructure, Distributed Systems, Azure Machine Learning, Data Streaming, Data Logging, Build Tools, Machine Learning Operations
- **Published:** October 15, 2026
- **Apply:** https://job-boards.greenhouse.io/reddit/jobs/8283770

## About the Role

* 5+ years of experience in the ML Platform domain.
 * Strong customer focus and experience driving projects from requirements to production in close collaboration with multiple cross-functional partners (XFN).
 * Experience with large-scale batch and streaming data processing.
 * Experience building production-quality systems with testing, monitoring, observability, and reliability practices.
 * Deep understanding of distributed systems and data-intensive architectures, with strong communication and collaboration skills.

## Description

As a Senior Software Engineer, ML Platform, you will work on our ML Data Team and be instrumental in architecting, implementing, and maintaining foundational ML infrastructure powering Feeds Ranking, Content Understanding, Recommendations, Ads, and much more to fulfill Reddit's mission of bringing community and belonging to everyone in the world. You will create ML data-governance tools that help MLEs discover and reason about signals, while contributing across the broader ML data platform-including prediction logging and training data systems.

 * Lead the design, development, testing, and operation of ML signal-governance capabilities that help MLEs discover signals and understand their ownership, lineage, quality, and usage.
 * Build tools that help teams assess signal importance and make informed lifecycle decisions.
 * Partner with MLEs and cross-functional teams to define governance workflows and deliver reliable, intuitive ways to explore and act on signal metadata.
 * Contribute across the ML Data Platform, including prediction logging and training data platforms, to design, develop, test, and operate batch and streaming pipelines processing petabyte-scale data.
 * Drive complex projects end to end from technical planning and design through implementation, testing, rollout, and ongoing operation.