MTS, Inference Performance Visibility

Posted: 2026-01-18

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.

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.

- 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.