Senior ML Platform Engineer - Scalable Graph & MLOps

Fdetimes; United States; Remote; USD 217,000 - 303,000; Full time

Benefits offered by this job

Equity in RSUs

Medical, dental, and vision insurance

401(k) with employer match

Job summary

Reddit’s Machine Learning Platform team seeks a Senior ML Infrastructure Engineer to lead a platform for large-scale ML models. You will design end-to-end MLOps patterns, enabling rapid development for ML engineers, and build a graph ML codebase to improve scalability and efficiency.

Key focus includes performance tuning, distributed training, and advanced data processing across cloud-based tools. Collaboration with ML engineers is essential to drive scalable ML solutions.

Qualifications

• 5+ years of experience in ML infrastructure, including model training and deployments.
• Hands-on experience with ML optimization, including memory and GPU profiling.
• Deep experience with cloud-based technologies supporting an ML platform (GCP, storage, IaC).
• Experience with MLOps tools for experiment tracking, model serving, and registries (MLflow/Wandb).
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Experience with distributed training (Ray, Kubernetes).
• Strong focus on scalability, reliability, performance, and user-centric design.

Responsibilities

• Design end-to-end model lifecycle patterns (MLOps) to boost velocity of ML development.
• Develop and support a graph ML codebase and platform for scalability and iteration.
• Collaborate on performance tuning, including GPU training costs in a large distributed environment.
• Optimize batch data processing with Beam, Spark, Ray Data, and related tools.
• Architect pipelines to manage large graph data structures with billions of nodes and edges.

Skills

ML infrastructure

Python

PyTorch

TensorFlow

GCP

Kubernetes

Ray

BigQuery

Terraform

MLflow

Wandb

Apache Spark

Apache Beam

Tools

MLflow

Wandb

Terraform

BigQuery

Google Cloud Storage

Apache Spark

Job description

Reddit’s Machine Learning Platform team seeks a Senior ML Infrastructure Engineer to lead a platform for large-scale ML models. You will design end-to-end MLOps patterns, enabling rapid development for ML engineers, and build a graph ML codebase to improve scalability and efficiency.

Key focus includes performance tuning, distributed training, and advanced data processing across cloud-based tools. Collaboration with ML engineers is essential to drive scalable ML solutions.