Machine Learning Infrastructure Engineer, GenAI Technology

Overview

Build and operate high-performance ML infrastructure and distributed systems for large-scale generative AI workloads.

What you'll do

• Design and implement infrastructure to support generative AI and ML workloads.
• Operate distributed systems for model training, hyperparameter tuning, inference, and data preprocessing.
• Collaborate with ML researchers and engineers to optimize compute utilization, throughput, and latency.
• Develop and automate deployment, orchestration, and CI/CD pipelines using container orchestration and IaC.
• Implement observability, monitoring, and cost-management for GPU and accelerator compute.
• Drive security, compliance, and operational runbooks for GenAI infrastructure.
• Troubleshoot, profile, and optimize performance across GPU and CPU compute stacks.

What you'll need

• 3–7 years of experience building and maintaining scalable compute or ML infrastructure systems.
• Bachelor’s or master’s degree in computer science, electrical engineering, or a related field.
• Deep understanding of distributed systems and Kubernetes.
• Hands-on experience with ML infrastructure tools including MLflow, Ray, Airflow, Kubeflow, and Terraform.
• Proficiency in Python and systems-level programming in Go, C++, or Rust.
• Strong debugging, profiling, and optimization skills across GPU/CPU compute stacks.

Details

• Annual base salary range: $180,000–$300,000 (USD).