# Staff Machine Learning Systems Engineer

**Company:** [Reddit](https://hotfix.jobs/companies/reddit)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $230k – $322k/yr
**Experience:** 8+ years
**Skills:** Python, PyTorch, TensorFlow, Kubernetes, Ray, MLflow, Wandb, Terraform, Spark, Apache Beam, Gcp Bigquery, Google Cloud Storage, Pytorch Geometric, Deep Graph Library, Neo4J
**Posted:** 2026-03-18

> Leads development of large-scale ML platforms, focusing on MLOps, graph ML infrastructure, performance optimization, and distributed training pipelines. Requires 8+ years in ML infrastructure with expertise in Python, PyTorch, Kubernetes, Ray, and cloud tools.

## Job Description

## What You’ll Do:

- 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

## Who You Might Be:

- **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

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**Apply:** https://hotfix.jobs/jobs/b21e2bdd-3dcf-4d69-80e0-49fe19d14ab1
**Canonical:** https://hotfix.jobs/jobs/b21e2bdd-3dcf-4d69-80e0-49fe19d14ab1