Performance Engineer

Performance Engineer About Etched 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. Key responsibilities - Develop comprehensive performance models and projections for Sohu's transformer-specific architecture across varying workloads and configurations - Profile and analyze deep learning workloads on Sohu to identify micro-architectural bottlenecks and optimization opportunities - Build analytical and simulation-based models to predict performance under different architectural configurations and design trade-offs - Collaborate with hardware architects to inform micro-architectural decisions based on workload characteristics and performance analysis - Drive hardware/software co-optimization by identifying opportunities where architectural features can unlock significant performance improvements - Characterize and optimize memory hierarchy performance, interconnect utilization, and compute resource efficiency - Develop performance benchmarking frameworks and methodologies specific to transformer inference workloads Key Responsibilities - Build detailed roofline models and performance projections for Sohu across diverse transformer architectures (Llama, Mixtral, etc.) - Profile production inference workloads to identify and eliminate micro-architectural bottlenecks - Analyze memory bandwidth, compute utilization, and interconnect performance to guide next-generation architecture decisions - Develop performance modeling tools that predict chip behavior across different batch sizes, sequence lengths, and model configurations - Characterize the performance impact of architectural features like specialized datapaths, memory hierarchies, and on-chip interconnects - Compare Sohu's architectural efficiency against conventional GPU architectures through detailed bottleneck analysis - Inform hardware design decisions for future generations (next gen and beyond) based on workload analysis and performance projections You may be a good fit if you have - 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 Strong candidates may also have - 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 Benefits - Medical, dental, and vision packages with generous premium coverage - $500 per month credit for waiving medical bene ... (truncated, view full listing at source)