Performance Engineer

Deep expertise in computer architecture and micro-architecture, particularly for accelerators or domain-specific architectures

Strong performance modeling and analysis skills with experience building analytical or simulation-based performance models

Experience profiling and optimizing deep learning workloads on hardware accelerators (GPUs, TPUs, ASICs, FPGAs)

Strong understanding of hardware/software co-design principles and cross-layer optimization

Solid foundation in digital circuit design and how micro-architectural decisions impact performance

Experience with reconfigurable or heterogeneous architectures

Ability to reason quantitatively about performance bottlenecks across the full stack from circuits to workloads

PhD or equivalent research experience in Computer Architecture or related fields

Experience with ASIC, FPGA, or CGRA-based accelerator development

Published research in computer architecture, ML systems, or hardware acceleration

Deep knowledge of GPU architectures and CUDA programming model

Experience with architecture simulators and performance modeling tools (gem5, trace-driven simulators, custom models)

Track record of informing architectural decisions through rigorous performance analysis

Familiarity with transformer model architectures and inference serving optimizations

Etched believes in the Bitter Lesson . We think most of the progress in the AI field has come from using more FLOPs to train and run models, and the best way to get more FLOPs is to build model-specific hardware. Larger and larger training runs encourage companies to consolidate around fewer model architectures, which creates a market for single-model ASICs.

We are a fully in-person team in San Jose (Santana Row), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both as needed.