Senior Staff Machine Learning Systems Engineer

Role Description

We are looking for a Senior Staff Machine Learning Systems Engineer to lead the technical strategy for the end-to-end Ads ML engineer lifecycle. The initial focus will be on the feature development and training iteration loop, making it faster and easier for ML engineers to build features, generate reliable training data, run experiments, and move from idea to validated model improvement.

• 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.
• 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.

Qualifications

• 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.
• Led broad, ambiguous, multi-team platform initiatives from strategy through adoption.
• Built 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.
• Worked with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, Databricks, or similar technologies.
• Ability to balance urgent customer needs with durable long-term architecture and reusable platform patterns.
• Influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms.
• Excited to shape how production ML systems are built, scaled, and operated, not only how models are trained.

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