Etched.ai is an AI infrastructure company building rack-scale and frontier inference systems.
About Etched 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. Job Summary Join our team and take the lead in illuminating the performance landscape of our cutting-edge ML accelerator. We are seeking a highly skilled engineer to design and develop a sophisticated performance analysis tool, tailored specifically for our hardware. You will be instrumental in creating the essential tooling that enables our ML engineers and customers to understand workload behavior, identify performance bottlenecks, and unlock the full potential of our hardware, accelerating the most demanding ML applications in the world. This is a unique opportunity to shape performance analysis for novel hardware from the ground up. Key responsibilities - Lead the design and architecture of a comprehensive performance analysis suite, including data collection mechanisms, data processing pipelines, analysis engines, and user interfaces (CLI and/or GUI). - Develop robust methods to capture performance data directly from our custom ML accelerator hardware (e.g., hardware performance counters, execution unit status, memory access patterns) via driver interfaces or other mechanisms. - Implement tracing for host-side API calls (runtime libraries, driver interactions) and system-level events (CPU activity, PCIe traffic, memory usage, network contention) related to our workloads. - Design and implement techniques to accurately correlate performance events across the host CPU, device driver, PCIe bus, multiple accelerators, and multiple hosts, ensuring precise time synchronization. - Build analysis modules to automatically interpret collected trace and counter data, identifying key performance limiters (e.g., compute-bound, memory bandwidth-bound, latency-bound, PCIe-bound, specific hardware bottlenecks). - Develop intuitive visualizations (timelines, dependency graphs, resource utilization charts, statistical summaries) to clearly communicate performance characteristics and bottlenecks to users. - Work closely with hardware architects, firmware engineers, driver developers, compiler engineers, and ML application engineers to understand their needs, define tool requirements, and provide expert guidance on performance analysis and optimization using the tool. Representative projects - Architect and implement the core data collection framework for hardware performance counters on a custom PCIe-based accelerator. - Develop a kernel driver module or user-space service for low-overhead tracing of accelerator activity. - Design and build a correlated timeline view visualizing CPU API calls, driver submissions, PCIe transfers, and accelerator execution units. - Create an analysis pass to detect and quantify memory access inefficiencies or PCIe bandwidth saturation while transacting on a PCIe-attached accelerator. You may be a good fit if you have - Strong proficiency in C++ or Rust - Proficiency in Python is a plus - Deep understanding of computer architecture (CPU, GPU, accelerators), memory hierarchies (caches, DRAM), and interconnects (especially PCIe). - Proven experience in low-level performance analysis, profiling, and bottleneck identification on complex hardware systems (GPUs, CPUs, FPGAs, or custom accelerators). - Experience with performance analysis tools (e.g., NVIDIA Nsight, AMD uProf, Intel VTune, perf, Tracy, ETW). - Experience working close to hardware, potentially reading performance counters or interacting directly with device drivers. Strong candidates may also have experience with (Nice-to-have qualifications) - Direct experience developing performance analysis or debugging tools. - Experience with ML accelerator architectures (GPUs, TPUs, etc.). - Experience with kernel-mode driver development (Linux or Windows). - Understanding of compiler internals, code generation, and optimization. - In-depth knowledge of the PCIe protocol and analysis tools (PCIe analyzers). - Experience with multi-chip or multi-host accelerator systems (e.g., TPU pods, or NVidia DGX clusters) - Experience with firmware or embedded systems development. - Experience with hardware description languages (Verilog, VHDL) or hardware verification. 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 - Unlimited compute budget subject to ROI justification How we’re different Etched believes in the Bitter Lesson http://www.incompleteideas.net/IncIdeas/BitterLesson.html. 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.