Senior Machine Learning Systems Engineer, Ads ML Experience Platform

Reddit

Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$216,700–$303,400 / yr
Posted
4 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $227k
This role $260k
$166k most similar roles pay here $318k

This role pays more than 78% of similar roles. Most pay $197,925–$256,803 — the shaded band above. At the midpoint, this role pays about $260k versus about $227k for comparable roles.

Based on 240 similar postings.

Employer

About Reddit

Reddit is a social news aggregation and discussion platform where users share content, vote on posts, and engage in community conversations across thousands of interest-based forums called subreddits.

Reddit currently has 118 open roles on FindRole.

Listed pay typically runs $217,000–$303,900 across 80 roles with salary data.

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At a glance

TL;DR · Senior Machine Learning Systems Engineer, Ads ML Experience Platform

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As a Senior Machine Learning Systems Engineer on the Ads ML Experience Platform team, you will design and build large-scale offline ML experimentation platforms that enable reproducible research and model development workflows. You’ll develop production-grade training orchestration frameworks supporting distributed training and hyperparameter optimization while also building infrastructure for experiment tracking and artifact versioning. Partnering with ML engineers and researchers, you will enhance operational efficiency and create automated workflows for continuous evaluation and compliance validation. The role involves deep expertise in large-scale distributed systems, modern orchestration technologies like Kubeflow and Argo, and experience with distributed data processing systems such as Spark or Flink. This position is integral to advancing the Ads ML lifecycle at Reddit by accelerating model iterations through scalable platform services and intelligent automation.

Skills

Kubeflow Argo Airflow Spark Flink Ray MCP A2A Python Docker CI/CD PostgreSQL Kubernetes Terraform AWS Grafana Prometheus

What you'll do

  • Design and build large-scale offline ML experimentation platforms for reproducible research.
  • Develop production-grade training orchestration frameworks supporting distributed training and hyperparameter optimization.
  • Build infrastructure for experiment tracking, metadata management, lineage, artifact versioning, and model registries.
  • Create automated workflows for continuous evaluation, compliance validation, and model promotion/rollback.
  • Design an agentic AI execution platform with autonomous and human-in-the-loop workflows.

What we're looking for

  • 5+ years of experience in infrastructure/platform engineering or large-scale distributed systems.
  • 2+ years hands-on experience building and operating production ML infrastructure, SDKs, APIs, or self-service AI tooling.
  • Experience with distributed data processing systems like Spark, Flink, Ray, or equivalent technologies.
  • Proficiency in modern orchestration and workflow technologies such as Kubeflow, Argo, Airflow, or similar frameworks.
  • Expertise in building offline ML experimentation platforms, model registries, experiment tracking systems, and training orchestration frameworks.

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