AI Networking Systems Software Engineer

AMD
Santa Clara, CA2026-04-16LAT_LNG

About the job

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.

Responsibilities

Work with AMD’s architecture specialists to improve future products

Apply a data minded approach to target optimization efforts

Stay informed of software and hardware trends and innovations, especially pertaining to algorithms and architecture

Design and develop new groundbreaking AMD technologies

Participating in new ASIC and hardware bring ups

Debugging/fix existing issues and research alternative, more efficient ways to accomplish the same work

Develop technical relationships with peers and partners

Design, develop, and optimize networking software for AMD Helios rack-scale systems

Enable and improve scale-up and scale-out networking performance for AI workloads

Collaborate with silicon, platform, firmware, driver, and systems teams to support new interconnect and networking features from concept through production

Drive hardware and software bring-up for networking subsystems, including link initialization, topology validation, performance tuning, and reliability testing

Analyze end-to-end network performance, latency, bandwidth utilization, congestion behavior, and communication efficiency across multi-node and rack-scale deployments

Develop tools, diagnostics, and telemetry frameworks to monitor network health, identify bottlenecks, and accelerate root cause analysis

Investigate and resolve issues across the stack, including NICs, switches, fabrics, transport layers, drivers, collectives libraries, and distributed runtime environments

Contribute to networking features that improve resiliency, scalability, serviceability, and deployment readiness in large-scale clusters

Partner with internal and external teams to validate interoperability and optimize AI workload performance on AMD platforms

Support performance characterization of communication libraries, collective operations, and distributed training or inference workloads over scale-up and scale-out fabrics

Help define software requirements and influence future hardware and system architecture for rack-scale networking solutions

Qualifications

Minimum

No minimum qualifications listed.

Preferred

Strong object-oriented programming background, C/C++ preferred

Ability to write high quality code with a keen attention to detail

Experience with modern concurrent programming and threading APIs

Experience with Windows, Linux and/or Android operating system development

Experience with software development processes and tools such as debuggers and source code control systems (GitHub) is a plus

Effective communication and problem-solving skills

Strong experience in Linux systems software and networking stack development

Experience with datacenter networking, distributed systems, or high-performance interconnect technologies

Familiarity with scale-up and scale-out architectures in AI or large distributed computing environments

Experience with network protocols and communication stacks such as Ethernet, RDMA, RoCE, InfiniBand, TCP/IP, or related high-performance transport technologies

Experience debugging complex multi-node system issues involving hardware, firmware, drivers, and application workloads

Understanding of performance analysis methodologies for latency-sensitive and bandwidth-intensive applications

Experience with profiling, tracing, and observability tools used for system and network performance tuning

Familiarity with collective communication libraries, distributed runtimes, or workload orchestration environments is a plus

Experience with ASIC, NIC, switch, or platform bring-up is highly desirable

Knowledge of reliability, availability, and serviceability considerations in rack-scale or datacenter deployments is a plus

Ability to work effectively across architecture, hardware, firmware, software, and validation teams