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 benefits
Housing subsidy of $2k per month for those living within walking distance of the office
Relocation support for those moving to San Jose (Santana Row)
Various wellness benefits covering fitness, mental health, and more
Daily lunch + dinner in our office
How we’re different
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.
etched