Senior ML Systems Engineer - Graph & MLOps Platform

Reddit; Oregon; Hybrid; USD 217,000 - 303,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with employer match

Generous time off

Parental leave

Job summary

Reddit's Machine Learning Platform team is hiring a senior ML infrastructure engineer to design scalable, end-to-end MLOps patterns and drive performance across large-scale training pipelines.

You will work with graph ML workloads, manage GPU profiling, and collaborate with engineers to optimize cloud resources (GCP) and data processing with Spark, Beam, and Ray. Join a high-impact, equity-offered environment that values reliability and user-focused tooling.

Qualifications

• 5+ years of experience in ML infrastructure including model training and deployments.
• Experience with GPU and memory profiling and performance tuning.
• Strong cloud experience (GCP) and infrastructure-as-code (Terraform).
• Experience administering MLOps tools for experiment tracking, model serving, and registries (MLflow, Wandb).
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow).

Responsibilities

• Design end-to-end ML infrastructure patterns (MLOps) to accelerate development for ML engineers.
• Develop and support a graph ML codebase and platform at scale.
• Collaborate with ML engineers on performance tuning including GPU training costs.
• Optimize batch data processing with Beam, Spark, Ray Data, etc.
• Architect pipelines for graphs with billions of nodes and edges.

Skills

ML infrastructure

GPU profiling

Cloud platforms

MLOps tools

Python

PyTorch

TensorFlow

Distributed training

Kubernetes

Communication skills

Tools

MLflow

Wandb

Terraform

GCP BigQuery

Google Cloud Storage

Ray

Job description

Reddit's Machine Learning Platform team is hiring a senior ML infrastructure engineer to design scalable, end-to-end MLOps patterns and drive performance across large-scale training pipelines.

You will work with graph ML workloads, manage GPU profiling, and collaborate with engineers to optimize cloud resources (GCP) and data processing with Spark, Beam, and Ray. Join a high-impact, equity-offered environment that values reliability and user-focused tooling.