Quick Summary
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.
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…
Etched is building hardware for frontier intelligence. We co-design chips, racks, software, and manufacturing to deliver best-in-class throughput and latency across both prefill and decode workloads. Our first products are heavily focused on inference. 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.
Responsibilities
~1 min read- →
Develop comprehensive performance models and projections for our architecture across varying workloads and configurations
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Profile and analyze deep learning workloads on our hardware 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
What We Offer
~1 min readEtched believes in the Bitter Lesson. We are the first inference-focused frontier AI system, betting early on transformer and transformer-like architectures and on increasing model sizes. Our addressable market is the entirety of inference, unlike many of our competitors.
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 and work across disciplines as needed.
Location & Eligibility
Listing Details
- Posted
- April 21, 2026
- First seen
- May 6, 2026
- Last seen
- June 25, 2026
Posting Health
- Days active
- 49
- Repost count
- 0
- Trust Level
- 26%
- Scored at
- June 25, 2026
Signal breakdown
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