Head of Performance Profiling

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.

Job Summary
We are hiring a Head of Performance Profiling to define how performance is understood across next-generation AI accelerator systems.

Our ML accelerator platform spans custom silicon, supercomputing software, compiler stacks, runtime libraries, and distributed inference environments. Performance at this scale is no longer a device-level question — it is a high-performance distributed system problem. You will define the performance metrics that connect raw hardware signals to distributed workload context, ML cluster dynamics, pod communication patterns, and emergent bottlenecks.

This role requires more than telemetry. You will establish new abstractions, structured counter ontologies, cross-layer event correlation frameworks, distributed time-alignment strategies, and scalable reasoning systems operating across nodes, racks, and clusters. Working at the intersection of hardware design, driver architecture, runtime systems, and ML infrastructure, you will shape how these layers expose and consume performance intelligence. This is a foundational role defining not just tooling, but how our platform reasons about efficiency, scalability, and system behavior for years to come.

Key Responsibilities
System-Level Performance Design

• Define the architectural approach for collecting and structuring telemetry across CPUs, drivers, interconnects, and multiple accelerators

• Design scalable models for correlating performance events across device and host boundaries

Cross-Layer Event Correlation

• Develop mechanisms to align hardware counters, runtime activity, communication phases, and workload semantics across model-layer execution into coherent, actionable insight

• Implement time synchronization and trace-alignment strategies across multi-device systems

Telemetry & Counter Modeling

• Define structured counter taxonomies separating base signals from derived metrics

• Design derived performance models bridging low-level hardware signals and workload-level behavior

• Influence instrumentation strategy for future hardware generations

Distributed Performance Reasoning

• Build tools that identify bottlenecks among multi-accelerator workloads across chips within hosts

• Build cluster-scale performance analysis for distributed inference across data center networks

Tooling & Insight Delivery

• Contribute to analysis engines and developer-facing tooling that transform raw telemetry into intuitive insight

• Shape how performance intelligence is surfaced to engineers debugging large-scale AI systems

You may be a good fit if you have

• Deep experience building complex systems at the intersection of hardware and software

• Personally envisioned and built significant portions of profiling, tracing, or observability systems — not solely defined requirements or product strategy

• Demonstrated ability to translate raw hardware signals into scalable, production-grade telemetry and analysis infrastructure

• Experience correlating time-series events across distributed systems

• Deep systems programming expertise (C++ or Rust), with a track record of shipping low-level infrastructure operating close to hardware or runtime systems

• Experience designing distributed correlation mechanisms, timestamp-alignment strategies, or performance modeling frameworks across multiple devices or hosts

• A history of introducing new technical abstractions or counter models that materially improved how engineers debug and optimize systems

• Experience designing distributed tracing or observability platforms at scale

• Experience with high-performance computing systems and large AI training clusters

• Experience with timestamp synchronization strategies and event alignment in distributed environments

• Experience with hardware counter design and instrumentation strategy

• Experience with runtime systems, compiler internals, or scheduling frameworks

• Experience with performance modeling for large-scale ML workloads

• Experience leading cross-functional architectural initiatives spanning hardware and software teams

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 and 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 and Taipei, 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.