Senior Staff Machine Learning Systems Engineer, Ads ML Platform

Responsibilities

• Own the technical strategy for the end-to-end Ads ML engineer lifecycle, beginning with feature development, training data, offline experimentation, and model iteration workflows.
• Align Ads ML platform priorities with Reddit’s broader ML Platform vision and translate Ads needs into reusable platform capabilities.
• Define architecture and technical standards for ML feature and training-data systems across batch and streaming computation, backfills, lineage, quality, observability, and online/offline consistency.
• Identify high-leverage friction points for ML engineers and improve development velocity.
• Build platform abstractions and workflow automation that make ML development faster, safer, more reliable, and self-service.
• Extend platform strategy into serving and online experimentation workflows.
• Partner across Ads, ML Platform, Data Platform, modeling, product, and engineering teams to clarify ownership and drive execution.
• Mentor Staff and senior engineers and raise architecture and operational standards.

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 by ML engineers, data scientists, or product teams developing production ML systems.
• Deep experience with ML platforms, feature platforms, training data, experimentation, developer infrastructure, or distributed data infrastructure.
• Ability to balance urgent customer needs with durable architecture and reusable platform patterns.
• Ability to influence senior engineers and leaders through technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms.

Nice-to-haves

• Experience with distributed data and compute technologies such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, or Databricks.
• Interest in shaping how production ML systems are built, scaled, and operated.

Compensation & Benefits

• Base salary range: $292,500–$409,500 USD.
• Equity in the form of restricted stock units; some positions may also be eligible for commission.
• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match.
• Global benefits supporting workspace, professional development, caregiving, and family planning.
• Gender-affirming care.
• Mental health and coaching benefits.
• Flexible vacation and paid volunteer time off.
• Generous paid parental leave.