Senior Staff Machine Learning Systems Engineer, Ads ML Platform

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Team Overview

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. Our systems help ML engineers move faster across the full development lifecycle: creating features, generating training data, running offline experiments, validating model quality, launching production models, and operating ML systems reliably.

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. Over time, this scope will expand into serving and online experimentation workflows, creating a more seamless path from offline iteration to production impact.

This is a senior technical leadership role for someone who can combine deep systems expertise, production ML experience, architectural judgment, and cross-team influence.

What You’ll Do

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.