Senior Staff Machine Learning Systems Engineer, Ads ML Platform

Reddit is seeking a Senior Staff Machine Learning Systems Engineer to lead the technical strategy for the end-to-end Ads ML engineer lifecycle.

The Ads ML Platform team builds infrastructure that accelerates high-scale ML systems and tooling for Ads ML, while extending reusable capabilities to broader Reddit ML use cases where appropriate.

Responsibilities:

• 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.

Requirements:

• 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems.
• 4+ years building or operating production ML infrastructure, feature platforms, training data systems, experimentation systems, or large-scale data pipelines.
• Experience leading broad, ambiguous, multi-team platform initiatives from strategy through adoption.
• Experience building platforms used directly by ML engineers, data scientists, or product teams developing production ML systems.
• Deep experience in ML platform, feature platform, training data, experimentation, developer infrastructure, or distributed data infrastructure.
• Experience working with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, Databricks, or similar technologies.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave