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ML Performance Characterization Engineer

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ML Performance Characterization Engineer

About Etched

Etched is building AI chips that are hard-coded for individual model architectures. Our first product (Sohu) only supports transformers, but has an order of magnitude more throughput and lower 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.

Job Summary

Join our team as a Senior ML Performance Characterization Engineer 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 Sohu. 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 Sohu 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

Tool Architecture & Design: 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).
Low-Level Data Collection: 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.
Host & System Tracing: 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 Sohu workloads.
Data Correlation & Synchronization: Design and implement techniques to accurately correlate performance events across the host CPU, device driver, PCIe bus, and multiple accelerators, ensuring precise time synchronization.
Performance Analysis Engine: 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).
Visualization & Reporting: Develop intuitive visualizations (timelines, dependency graphs, resource utilization charts, statistical summaries) to clearly communicate performance characteristics and bottlenecks to users.
Collaboration & Support: 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/C++ and Python.
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 ASICs).
Experience with performance analysis tools (e.g., NVIDIA Nsight, AMD uProf, Intel VTune, perf, Tracy, ETW).
Solid understanding of operating system internals (Linux preferred), including scheduling, memory management, and driver interaction.
Experience working close to hardware, potentially reading performance counters or interacting directly with device drivers.

Strong candidates may also have experience with

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 firmware or embedded systems development.
Experience with hardware description languages (Verilog, VHDL) or hardware verification.

Benefits

Full medical, dental, and vision packages, with 100% of premium covered
Housing subsidy of $2,000/month for those living within walking distance of the office
Daily lunch and dinner in our office
Relocation support for those moving to West San Jose

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 West San Jose, 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.

Average salary estimate

$135000 / YEARLY (est.)

min

max

$120000K

$150000K

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What You Should Know About ML Performance Characterization Engineer, Etched

Are you ready to dive into the world of machine learning and play a pivotal role at Etched? As our new ML Performance Characterization Engineer, you'll lead the charge in analyzing and enhancing the performance of our groundbreaking ML accelerator known as Sohu. This role in San Jose presents an exciting opportunity for you to design and develop a sophisticated performance analysis toolkit tailored just for Sohu, enabling fellow engineers and customers alike to grasp the intricate workload behaviors that drive today’s most demanding ML applications. Picture yourself architecting a comprehensive suite that covers everything from low-level data collection to powerful analysis engines and visually engaging user interfaces. Your expertise will allow you to capture and interpret data from custom accelerator hardware, ensuring optimal performance in diverse and challenging environments. Whether you're developing robust methods for data collection or crafting intuitive visualizations that reveal key performance characteristics, your contributions will have a significant impact on Sohu and its deployment across the industry. If you’re passionate about computer architecture, have experience with performance tools, and thrive on collaboration, we’d love to have you on our team at Etched. Together, we can push the boundaries of what’s possible in machine learning and create products that redefine industries.

Frequently Asked Questions (FAQs) for ML Performance Characterization Engineer Role at Etched

What responsibilities does a ML Performance Characterization Engineer have at Etched?

At Etched, an ML Performance Characterization Engineer is responsible for designing and developing a comprehensive performance analysis suite specifically for our ML accelerator, Sohu. Key responsibilities include tool architecture, data collection mechanisms, and performance analysis engines. This role involves collaborating closely with hardware and software teams to identify performance bottlenecks and enhance the efficiency of our machine learning applications.