ML Engineer - Infrastructure

What you'll do

• Architect, operate, and improve cloud-based GPU clusters, including provisioning, scheduling, and monitoring.
• Help AI engineers optimize training workloads and hardware utilization, including contributing to core ML libraries.
• Contribute to GPU compute strategy, multi-cloud capacity and cost optimization, and engineering reliability practices.

What they ask for

• Degree in Computer Science, Electrical Engineering, or Software Engineering, or equivalent practical experience, plus significant industry experience.
• Hands-on experience training or serving billion-parameter models on distributed multi-node GPU hardware.
• Proficiency in Python; working knowledge of PyTorch; deep understanding of distributed training concepts (DDP, FSDP, NCCL).
• Experience with a cloud platform or large-scale on-premises GPU infrastructure, and with Slurm and/or Kubernetes/KubeRay.

Nice to have

• Familiarity with profilers such as PyTorch Profiler, Dynolog, HTA, or Nsight; experience with high-performance or parallel file systems such as Lustre.
• Experience provisioning compute across multiple cloud providers, or with infrastructure-as-code and configuration management such as Terraform and Ansible.

Tools

Python, PyTorch, DDP, FSDP, NCCL, AWS, GCP, Azure

AI focus

Infrastructure for training foundation models and supporting AI model development, including distributed training and inference, workload profiling, and hardware-utilization optimization.