Cloud ML/HPC Networking Engineer (C, Open Source)

Amazon Inc.; Seattle (WA); On-site; USD 144,000 - 194,000; Full time

Benefits offered by this job

Health insurance (medical, dental, and

401(k) matching

Paid time off

Parental leave

RSUs

Job summary

Amazon Inc. in Seattle is building the next generation of ML-ready cloud networking. You will craft the highest-performing C code for EFA-related projects such as Libfabric and Open MPI, collaborating across teams to design APIs for cutting-edge cloud networking and for customers running thousands of GPU and CPU instances.

A day in the life includes designing efficient software, writing tests, and ensuring performance while working with the ML Infrastructure team on large clusters.

Qualifications

• 3+ years of non-internship professional software development experience.
• 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
• 3+ years of professional experience programming high-performance software in C, ideally as part of an Open Source project

Responsibilities

• You will write the highest-performing code in C for multiple open source projects supporting EFA.
• You will work with multiple teams to invent new APIs for cloud networking.
• Dive deep into collectives and messaging at high bandwidth and low latency for customers.
• Provide expert-level support to major AI customers.

Skills

3+ years professional software in C

2+ years design/architecture

3+ years high-performance C

Education

Bachelor's degree in computer science or equivalent

Tools

Libfabric

Open MPI

Job description

Amazon Inc. in Seattle is building the next generation of ML-ready cloud networking. You will craft the highest-performing C code for EFA-related projects such as Libfabric and Open MPI, collaborating across teams to design APIs for cutting-edge cloud networking and for customers running thousands of GPU and CPU instances.

A day in the life includes designing efficient software, writing tests, and ensuring performance while working with the ML Infrastructure team on large clusters.