AWS's Trainium and Inferentia chips power the world's largest machine learning clusters. Our team builds virtual platforms — full-system C++ and SystemC models of these custom SoCs — that let software teams start development months before silicon arrives. For Trainium3, our virtual platform enabled running a full training workload within 12 hours of first silicon. We're looking for a software engineer to build and own the models and infrastructure that make this possible. Why this role is interesting: - You'll own a product that software teams across AWS depend on — they literally can't start development without your virtual platform - The engineering challenges are genuinely interesting: full-system simulation, multi-subsystem integration, QEMU development, performance optimization at scale - You'll see the direct impact of your work when software teams hit the ground running on new silicon - As the team grows, there's a path into architectural modeling — using the platform to explore design alternatives and influence chip architecture - Small team, startup pace, big impact inside AWS's custom silicon org You will thrive in this role if you: - Have built functional models, virtual platforms, or system-level simulations for SoCs, ASICs, GPUs, or CPUs - Think of yourself as a software engineer first, with deep domain knowledge in chip architecture - Are comfortable in C++ or SystemC, and familiar with Python for tooling - Care about your customers' experience — you think about usability, documentation, and reliability, not just model accuracy - Are interested in expanding into performance or architectural modeling as the team scales - Enjoy working on a small, high-impact team where you own significant pieces of the stack No ML background needed. You'll learn the ML accelerator domain on the job. This role can be based in Cupertino, CA or Austin, TX.
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Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed