Software Engineer, ML Networking

Role Summary

A systems-level engineer specializing in network infrastructure and network optimization, with expertise in building and maintaining software that interacts with networks. You will be responsible for writing and maintaining software that interfaces between our accelerators and our high-speed networks. This role requires deep technical knowledge of network protocols, kernel-space and/or user-space networks, interfacing with hardware, and the ability to debug and optimize distributed software at the network level.

You may be a good fit if you have:

Networking Systems Engineering:

• Expert-level proficiency with network protocols and networking concepts
• Deep kernel networking: TCP/IP stack internals, XDP, eBPF, io_uring, and epoll
• User-space networking: DPDK, RDMA, kernel bypass techniques
• Understanding of how to build higher-level abstractions like collectives and RPC
• Skilled at diagnosing and resolving networking issues in distributed systems, especially at OSI model layers 2-4

Low-Level Systems and OS Programming:

• Strong programming skills in a systems programming language, including memory management, lock-free data structures, and NUMA-aware programming
• Software, driver, and OS performance optimization tools and techniques
• Comfort with or desire to learn Rust

Strong candidates may have:

• Understanding of ML accelerators and accelerator drivers
• Demonstrated ability to design new network protocols
• Experience with PCIe and drivers for PCIe devices
• Expertise in algorithms used in networking, including compression and graph algorithms
• Experience programming on SmartNICs
• 5+ years of experience in systems programming or network programming
• Often comes from backgrounds in: HPC, telecommunications, host networking software, OS/kernel engineering, or embedded systems
• Strong debugging mindset with patience for complex, multi-layered issues

Representative Projects:

• Build a system for accelerator-initiated tensor movement over the network
• Benchmark software for a new networking environment
• Implement a new collective algorithm to improve latency
• Optimize congestion control algorithms for large-scale synchronous workloads
• Debug kernel-level network latency spikes