> Markdown version of [/jobs/ext/2710167-machine-learning-systems-engineer](https://www.wearedevelopers.com/jobs/ext/2710167-machine-learning-systems-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Systems Engineer

- **Company:** reddit Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $216,700.0 - $303,400.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Distributed Computing Environment, Distributed Systems, Machine Learning, Meta-Data Management, Tensorflow, Azure Machine Learning, Management of Software Versions, Workflow Management Systems, Multi-Agent Systems, Apache Spark, Model Validation, Core Api, Build Management, Kubernetes, Apache Flink, Machine Learning Operations
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-machine-learning-systems-engineer-ads-ml-experience-platform-reddit-8972482

## About the Role

* 5+ years 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, autonomous workflows, and agent communication/runtime frameworks (e.g., MCP, A2A, and orchestration systems) is a strong plus
 * Experience running end-to-end model development and iteration cycles at scale is a plus

## Description

We are building the next generation of ML research tools and agentic AI platforms that power machine learning development across Reddit. Our mission is to accelerate the Ads ML lifecycle - from experimentation and training to deployment, evaluation, and autonomous operations - through scalable platform services, intelligent automation, and developer-centric tooling.

Our team owns critical platform capabilities including offline ML experimentation systems, production training orchestration frameworks, ML lifecycle automation and, agentic ML frameworks that enable faster model iterations.

We are looking for an experienced engineer with deep expertise in large-scale distributed systems, ML platforms, and emerging agentic architectures to help define and build the foundational tooling for the next generation of our machine learning devX tooling.

What You'll Do

 * Design and build large-scale offline ML experimentation platforms that enable reproducible research, model development, evaluation, and promotion workflows.
 * Develop production-grade training orchestration frameworks supporting distributed training, hyperparameter optimization, model evaluation, and automated retraining.
 * Build infrastructure for experiment tracking, metadata management, lineage, artifact versioning, model registries, and reproducibility.
 * Partner with ML engineers and researchers to improve experimentation velocity and operational efficiency.
 * Build automated workflows for model promotion, rollback, compliance validation, and continuous evaluation.
 * Design and build an agentic AI execution platform supporting autonomous and human-in-the-loop workflows, including multi-agent orchestration, memory/context systems, and scalable workflow infrastructure.


## Related Videos

- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [Insights from building the Canva Developers Platform to empower 185 million designers](https://www.wearedevelopers.com/videos/942-insights-from-building-the-canva-developers-platform-to-empower-185-million-designers)
- [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs)
- [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning)
- [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale)
- [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)