AI/ML Network Infrastructure Engineer I

Amazon; Cupertino (CA); On-site; USD 127,000 - 185,000; Full time

Job summary

Annapurna Labs (U.S.) Inc. in Cupertino, CA is seeking an early-career engineer to own the network stack for EC2 distributed AI/ML systems. You will contribute to software enabling training of world-scale AI models across massive GPU clusters, supporting NCCL, NVSHMEM, and NIXL.

This role sits at the intersection of HPC networking and ML infrastructure, building systems powering the largest AI workloads in the cloud, with opportunities to influence cross-stack design and performance optimization.

Qualifications

• Bachelor's degree or above in Computer Science, Computer Engineering, or related fields.
• Strong proficiency in C/C++.
• Familiarity with Linux development environments and toolchains.
• Knowledge of operating systems, parallel computer architecture, and distributed systems is a plus.

Responsibilities

• Write high-performance C/C++ code for network communication libraries running on custom AWS hardware
• Build and maintain infrastructure that monitors functionality and performance of large-scale AI/ML workloads
• Develop automation using Python and AWS tools (CI/CD, Grafana, Athena) to test, benchmark, and deliver software to customers
• Design mechanisms to detect functional and performance regressions before they reach production
• Work across many instance types, software stacks, and Linux environments

Skills

C/C++

Linux development environments

Network programming exposure

GPU programming concepts

Education

Bachelor's degree or above in Computer Science, Computer Engineering, or related fields

Tools

NCCL

NVSHMEM

NIXL

Job description

Annapurna Labs (U.S.) Inc. in Cupertino, CA is seeking an early-career engineer to own the network stack for EC2 distributed AI/ML systems. You will contribute to software enabling training of world-scale AI models across massive GPU clusters, supporting NCCL, NVSHMEM, and NIXL.

This role sits at the intersection of HPC networking and ML infrastructure, building systems powering the largest AI workloads in the cloud, with opportunities to influence cross-stack design and performance optimization.