Senior Machine Learning Systems Engineer, Ads ML Experience Platform
Quick summary
- Work type
- Remote
- Location
- Remote
- Salary
- $216,700–$303,400 / yr
- Posted
- 32 days ago
- Freshness
- Confirmed live yesterday
Market check
Salary context
How this pay compares to similar roles
This role pays more than 78% of similar roles. Most pay $202,800–$256,237 — the shaded band above. At the midpoint, this role pays about $260k versus about $230k 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 77 open roles on FindRole.
Listed pay typically runs $217,000–$303,400 across 77 roles with salary data.
Most-posted roles
- Software Engineer 19
- Data Scientist 8
- Machine Learning Engineer 5
- Frontend Engineer 4
- Machine Learning Systems Engineer 4
At a glance
TL;DR · Senior Machine Learning Systems Engineer, Ads ML Experience Platform
Senior Machine Learning Systems Engineer, Ads ML Experience Platform joins the team building next-generation machine learning research tools and agentic AI platforms to accelerate the Ads ML lifecycle. You will design and build large-scale offline experimentation platforms for reproducible research, develop production-grade training orchestration frameworks for distributed training and hyperparameter optimization, and construct infrastructure for experiment tracking, metadata management, and model registries. Additionally, you will build an agentic AI execution platform supporting multi-agent orchestration and memory systems. The role requires expertise in large-scale distributed systems, developer SDKs, and automated workflows. Key technologies include Spark, Flink, Ray, Kubeflow, Argo, and Airflow. You will solve complex problems regarding model promotion, compliance validation, and autonomous operations to improve experimentation velocity and operational efficiency for researchers and engineers within the advertising machine learning domain.
Skills
What you'll do
- Design and build large-scale offline ML experimentation platforms for reproducible research and model development.
- Develop production-grade training orchestration frameworks supporting distributed training and hyperparameter optimization.
- Build infrastructure for experiment tracking, metadata management, lineage, and artifact versioning.
- Create automated workflows for model promotion, rollback, compliance validation, and continuous evaluation.
- Design and build an agentic AI execution platform supporting multi-agent orchestration and memory systems.
- Develop scalable workflow infrastructure to support both autonomous and human-in-the-loop processes.
What we're looking for
- 5+ years of experience in infrastructure/platform engineering or large-scale distributed systems.
- 2+ years of hands-on experience building and operating production ML infrastructure, developer SDKs, platform APIs, or self-service AI tooling.
- Experience building workflow orchestration systems, developer platforms, or large-scale automation frameworks.
- Experience with distributed data processing systems such as Spark, Flink, Ray, or equivalent technologies.
- Experience with modern orchestration and workflow technologies such as Kubeflow, Argo, Airflow, or similar frameworks.
- Experience building offline ML experimentation platforms, model registries, experiment tracking systems, or training orchestration frameworks.
- Experience building and operating agentic AI systems, including multi-agent orchestration and autonomous workflows (preferred).
- Experience running end-to-end model development and iteration cycles at scale (preferred).
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