Senior Machine Learning Engineer, Ads ML Platform

Reddit, Inc. is a community-driven platform built around shared interests, conversations, and authentic content. The Senior Machine Learning Engineer will build and scale data infrastructure, feature management systems, and training set platforms supporting Ads ML, while driving governance, automation, reliability, and integration with production ML systems.

Responsibilities:

• Design and build data infrastructure that supports large-scale feature and training set computation, transformation, and storage
• Develop frameworks for batch and real-time features with a focus on reliability, scalability, and ease of use
• Build platform capabilities for feature governance, including lineage tracking, validation, drift detection, anomaly monitoring, reproducibility, and versioning
• Partner with ML engineers to ensure smooth integration of feature engineering workflows into ML production systems
• Build systems that support agentic ML workflows, including automated feature discovery, feature quality evaluation and feature lifecycle management
• Drive architecture and technical direction for high-scale ML platform infrastructure powering ads ranking, targeting, and optimization systems
• Contribute to operational excellence through observability, performance tuning, reliability engineering, and cost optimization initiatives

Requirements:

• 5+ years in infrastructure/platform engineering or large-scale distributed systems
• 2+ years of hands-on experience building or operating ML platform infrastructure and production ML systems
• Proficiency with large-scale feature computation frameworks (Spark, PySpark, or Scala)
• Expertise in distributed systems (scaling, partitioning, fault tolerance, caching)
• Experience building intelligent automation or agentic workflows for ML systems is a strong plus, including areas such as automated pipeline management, feature recommendations, evaluation systems, or AI-assisted developer tooling
• Experience with ML infrastructure and MLOps workflows spanning feature engineering, training pipelines, experimentation, model deployment, and online serving is a plus