Performance Modeling Engineer

Etched is building the world’s first AI inference system purpose-built for transformers - delivering over 10x higher performance and dramatically lower cost and latency than a B200. With Etched ASICs, you can build products that would be impossible with GPUs, like real-time video generation models and extremely deep & parallel chain-of-thought reasoning agents. Backed by hundreds of millions from top-tier investors and staffed by leading engineers, Etched is redefining the infrastructure layer for the fastest growing industry in history.

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  Develop comprehensive performance models and projections for Sohu's transformer-specific architecture across varying workloads and configurations

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  Profile and analyze deep learning workloads on Sohu to identify micro-architectural bottlenecks and influence optimization opportunities

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  Drive hardware/software co-optimization by identifying where architectural features can unlock performance improvements

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  Run regressions and validate performance models against real systems and silicon

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  Inform next-generation architectural decisions by pathfinding across system and silicon options during design, proof-of-concept, and architecting phases

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

• Solid understanding of computer architecture and micro-architecture, particularly for accelerators

• Experience profiling and analyzing deep learning workloads on hardware accelerators (GPUs, TPUs, ASICs, FPGAs, or others)

• Solid software engineering fundamentals with an eye toward auditability and maintainability

• Deep knowledge of GPU architectures and/or programming models like CUDA

• Experience mapping models to multi-chip inference systems

• Familiarity with transformer model architectures and inference serving optimizations

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

• Exposure to ASIC, FPGA, or CGRA-based accelerator development and hardware/software co-design principles

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

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.