Senior ML Infrastructure Engineer PyTorch Kubernetes GPU Training
Overview
Design and scale infrastructure for large-scale machine learning training workloads. Build high-performance GPU training platforms and optimize distributed training pipelines. Improve developer experience for ML researchers. This role focuses on cloud-native solutions using AWS and Kubernetes.
What You'll Do5
- 1Design and scale distributed ML training infrastructure for large GPU clusters
- 2Build high-performance GPU training platforms using PyTorch and Kubernetes
- 3Optimize distributed training pipelines for efficiency
- 4Improve developer experience for ML researchers
- 5Ship scalable solutions that support rapid model iteration
Requirements5
- 15+ years building ETL pipelines with Spark and Airflow
- 2Experience with PyTorch and Kubernetes
- 3Knowledge of AWS services for distributed computing
- 4Proficiency in container orchestration with Kubernetes
- 5Understanding of GPU training frameworks and performance tuning
Salary Insight
$250 - $320k per year
Location
Required Skills
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