Senior Staff ML Platform Engineer

Aimlroles; Northern (KY); Hybrid; USD 293,000 - 410,000; Full time

Benefits offered by this job

Comprehensive Healthcare Benefits

401k with Employer Match

Generous Time Off

Job summary

Reddit is seeking a Senior Staff Machine Learning Systems Engineer to lead the end-to-end Ads ML engineer lifecycle, from feature development to training data and offline experiments. You’ll define architecture, standards, and reusable platform capabilities that accelerate ML work across teams.

You will mentor engineers, shape platform strategy, and partner across Ads, ML Platform, Data Platform, and modeling teams to deliver durable, scalable solutions for production ML systems.

Qualifications

• 8+ years in infrastructure, distributed systems, ML platforms, or large-scale backends.
• 4+ years building/operating production ML infrastructure or feature platforms.
• Led multi-team platform initiatives from strategy through adoption.

Responsibilities

• Own the end-to-end Ads ML engineer lifecycle strategy: feature development, data, offline experiments, model iteration.
• Align Ads ML priorities with Reddit's ML Platform vision to create reusable capabilities.
• Define architecture and standards for ML feature and training-data systems across batch/streaming, lineage and observability.
• Work closely with ML engineers and platform customers to reduce friction and speed development.
• Build platform abstractions and automation to make ML development faster, safer, and more self-service.
• Extend platform strategy into serving and online experimentation workflows to connect offline to online impact.
• Collaborate with Ads, ML Platform, Data Platform, modeling, product, and engineering teams; mentor senior staff.

Skills

Infrastructure

Distributed systems

ML platforms

Data platforms

Production ML

Platform leadership

Tools

Spark

Flink

Kafka

Ray

Airflow

Kubernetes

BigQuery

Snowflake

Databricks

Job description

Reddit is seeking a Senior Staff Machine Learning Systems Engineer to lead the end-to-end Ads ML engineer lifecycle, from feature development to training data and offline experiments. You’ll define architecture, standards, and reusable platform capabilities that accelerate ML work across teams.

You will mentor engineers, shape platform strategy, and partner across Ads, ML Platform, Data Platform, and modeling teams to deliver durable, scalable solutions for production ML systems.