Senior Staff Machine Learning Systems Engineer, Ads ML Platform

• Own the technical strategy for the end-to-end Ads ML engineer lifecycle, starting with feature development, training data, offline experimentation, and model iteration workflows.
• Align Ads ML platform priorities with Reddit’s broader ML Platform vision, translating Ads pain points into reusable platform capabilities where appropriate.
• Define architecture and technical standards for ML feature and training-data systems across batch/streaming computation, backfills, lineage, quality, observability, and online/offline consistency.
• Stay close to ML engineers and platform customers to identify high-leverage friction points and improve day-to-day development velocity.
• Build platform abstractions and workflow automation that make ML development faster, safer, more reliable, and more self-service.
• Over time, extend the platform strategy into serving and online experimentation workflows, creating a more seamless offline-to-online ML development experience.
• Partner across Ads, ML Platform, Data Platform, modeling, product, and engineering teams to clarify ownership, resolve ambiguity, and drive durable execution.
• Mentor Staff and senior engineers, raise the architecture and operational bar, and help grow the next generation of technical leaders.

Who You Might Be

• You have 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems.
• You have 4+ years building or operating production ML infrastructure, feature platforms, training data systems, experimentation systems, or large-scale data pipelines.
• You have led broad, ambiguous, multi-team platform initiatives from strategy through adoption.
• You have built platforms used directly by ML engineers, data scientists, or product teams developing production ML systems.
• You have deep experience in ML platform, feature platform, training data, experimentation, developer infrastructure, or distributed data infrastructure.
• You have worked with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, Databricks, or similar technologies.
• You can balance urgent customer needs with durable long-term architecture and reusable platform patterns.
• You influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms.
• You are excited to shape how production ML systems are built, scaled, and operated, not only how models are trained.