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# Staff Machine Learning Systems Engineer

- **Company:** reddit Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $230,000.0 - $322,000.0
- **Contract:** Permanent contract
- **Skills:** BigQuery, Cloud Computing, Cloud Storage, Data Cleansing, Data Structures, Data Warehousing, Distributed Computing Environment, Graph Database, Python (Programming Language), Machine Learning, Neo4j, Performance Tuning, Tensorflow, Azure Machine Learning, TigerGraph, Data Processing, Graph Neural Networks, Pytorch, Apache Spark, Kubernetes, Machine Learning Operations, Terraform, Apache Beam, Programming Languages
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_02dac831f271403164f490cfec0d47d9

## About the Role

* 8+ years of experience in ML infrastructure, including model training and model deployments
 * Hands-on experience with ML optimization, including memory and GPU profiling
 * Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and more
 * Hands-on experience administering and integrating MLOps tools for experiment tracking, model serving, and model registries (e.g. MLflow or Wandb)
 * Proficiency with the common programming languages and frameworks of ML, such as Python, PyTorch, Tensorflow, etc.
 * Deep experience working with distributed training frameworks, including Ray and Kubernetes
 * Strong focus on scalability, reliability, performance, and ease of use. You are an undying advocate for platform users and have a deep intuition for the machine learning development lifecycle.
 * Strong organizational & communication skills
 * Experience working with graph databases (Neo4j, JanusGraph, TigerGraph) is a big plus
 * Experience working with graph neural networks (GNNs) and associated graph ML frameworks (PyTorch Geometric, Deep Graph Library) is a big plus

## Description

As a Staff ML Infrastructure Engineer, you will lead development of a platform for large scale ML models at Reddit.

 * Design end-to-end model lifecycle patterns (MLOps) to boost velocity of development for ML engineers, including data preparation, model management, experiment tracking, and more
 * Zero-to-one development and support of a graph ML codebase and platform that abstracts away common patterns and enables greater model scalability and iteration
 * Collaborate with ML engineers on performance tuning, including improving model training time, efficiency, and GPU training costs in a large, distributed ML training environment
 * Optimize batch data processing within a data warehouse and with tools such as Apache Beam, Apache Spark, Ray Data, and more
 * Architect pipelines to build and maintain massive graph data structures on the order of billions of nodes and tens of billions of edges