Amazon PHD SDE - Systems Runtime ML Infrastructure AWS 

United States, Washington, Seattle 

507755625

We operate at an unprecedented scale, designing custom silicon chips, advanced networking solutions, and ML accelerators that were unimaginable just a few years ago.
Key job responsibilities
- Develop and optimize software for custom hardware and ML infrastructure- Implement and improve networking, runtime, and system-level software
- Assist in building and maintaining tools for profiling, monitoring, and debugging ML workloads
- Contribute to the development of open-source ML frameworks and infrastructure projects
- Participate in code reviews and implement best practices for software development
- Learn and apply new technologies to solve complex engineering challenges
- The Elastic Network Adapter (ENA) team revolutionizes EC2 core networking, enabling enhanced networking capabilities across AWS's most critical compute instances. Here, you'll work with networking protocols and high-performance drivers that power millions of cloud workloads.

- To qualify, applicants should have earned (or expect to earn) a PhD degree between December 2022 to September 2025
- Research in systems, computer architecture, networking, or related areas, demonstrated through publications, internships, or projects
- Skilled in C/C++ and Python, with expertise in implementing complex algorithms and data structures
- Understanding of computer architecture, operating systems, and low-level system optimizations, preferably with hands-on experience in Linux environments

- Research contributions or expertise in systems for ML, compilers, distributed computing, or hardware acceleration, demonstrated through publications or open-source projects- Experience with modern ML infrastructure, including frameworks (PyTorch/TensorFlow), compilers (XLA, MLIR), or hardware accelerators