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Staff Product Development Engineer - ATE Content Developer
Исключительно привлекательная вакансия в одном из самых перспективных ИИ-стартапов под руководством легенд индустрии. Высокий уровень компенсации и работа с передовыми технологиями (RISC-V, Chiplets) делают это предложение топовым на рынке.
Сложность вакансии
Роль требует глубоких экспертных знаний в области ATE (Advantest V93K), DFT-архитектур и опыта работы с передовыми техпроцессами. Высокая сложность обусловлена необходимостью работы с многокристальными (chiplet) ИИ-системами и внедрением SSN.
Анализ зарплаты
Указанный в вакансии диапазон ($100k - $500k) крайне широк, так как охватывает позиции разного уровня. Для уровня Staff Engineer в Остине или Санта-Кларе рыночная медиана составляет около $210,000 - $240,000 (base + bonus), что полностью соответствует предложению компании.
Сопроводительное письмо
I am writing to express my strong interest in the Staff Product Development Engineer position at Tenstorrent. With over 7 years of experience in semiconductor testing and a deep background in Advantest V93K platforms, I have a proven track record of delivering optimized production test programs for complex digital devices. My expertise in DFT/ATPG flows, specifically with scan chains and MBIST, aligns perfectly with your mission to revolutionize AI/ML silicon performance.
I am particularly excited about the opportunity to work with Streaming Scan Network (SSN) architectures and multi-die chiplet designs. Having automated numerous test flows using Python and C++, I am confident in my ability to reduce test costs while maintaining high coverage targets. I look forward to bringing my technical rigor and passion for high-performance computing to the Tenstorrent team.
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Описание вакансии
Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities.
Tenstorrent is looking for a Staff Product Development Engineer - ATE Content Developer, who will be focused on core content development for high-performance AI/ML silicon. They will be responsible for developing production test programs on ATE test platforms, translating DFT/ATPG content into optimized ATE solutions, and implementing Streaming Scan Network (SSN) architectures for efficient test data delivery. High level challenges include reducing test cost while maintaining coverage targets, optimizing test time for chiplet and multi-die architectures, and enabling rapid yield learning through robust test methodologies. The work is done collaboratively with a group of highly experienced engineers across DFT, design, product, and manufacturing domains.
This role is hybrid, based out of Austin, TX or Santa Clara, CA.
We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.
Who You Are
- An experienced semiconductor test engineer with 7 years and a BS/MS in EE/ECE/CE and a track record testing complex digital devices on advanced nodes.
- Hands-on with Advantest V93K and/or Teradyne UltraFlex+ platforms, comfortable owning production test programs end‑to‑end.
- Deeply familiar with DFT/ATPG flows and test architectures: scan chains, MBIST, compression, JTAG/IEEE 1149.1, and common fault models (stuck‑at, transition, path delay, cell‑aware).
- Proficient in C/C++ or Java, with strong scripting skills in Python, Perl, or TCL to automate flows, pattern handling, and data analysis.
- Skilled at debugging across ATE hardware, test programs, and silicon, and at using data to drive root‑cause analysis and yield improvement.
What We Need
- Develop and optimize production test programs on Advantest V93K using the SmarTest 8 environment, from bring‑up through high‑volume manufacturing.
- Translate ATPG patterns (STIL/WGL) into production‑ready test content, balancing test time, coverage, and cost for chiplet and multi‑die AI/ML devices.
- Implement and debug Streaming Scan Network (SSN) based content for high‑speed scan delivery, ensuring robust and scalable scan test infrastructure.
- Own test content for scan, BIST, and memory test structures, collaborating with DFT teams on pattern debug, fault diagnosis, and coverage improvement.
- Support silicon bring‑up and debug in the lab and at manufacturing sites, including correlation, corner characterization, and PVT studies.
- Build and maintain automation scripts and test methods that improve productivity, repeatability, and quality across the test lifecycle.
- Document test architectures, flows, and debug procedures so they can be scaled across products, sites, and engineering teams.
What You Will Learn
- How to test and scale cutting‑edge AI/ML silicon with chiplet and multi‑die architectures, including exposure to 2.5D/3D packaging and HBM integration.
- Deeper expertise in SSN architectures, high‑speed scan delivery, and next‑generation DFT/test methodologies for complex HPC/AI devices.
- How to drive end‑to‑end yield learning: from ATE data and STDF analytics through to design, DFT, and manufacturing process feedback.
- How a fast‑growing AI hardware startup coordinates DFT, design, product, and manufacturing to bring new silicon from first silicon to high‑volume production.
- Opportunities to influence test strategy, infrastructure, and automation roadmaps for future products and platforms as Tenstorrent’s portfolio scales.
Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made.
Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.
This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
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Навыки
- C++
- Python
- DFT
- Java
- TCL
- Perl
- JTAG
- ATPG
- RISC-V
- MBIST
- Advantest V93K
- SmarTest 8
- STIL
- WGL
- SSN
Возможные вопросы на собеседовании
Проверка практического опыта работы с основной платформой, указанной в вакансии.
Опишите ваш опыт разработки тестовых программ в среде SmarTest 8 для Advantest V93K. С какими основными трудностями вы сталкивались при отладке?
Вакансия делает упор на новые технологии доставки тестовых данных.
Как вы подходите к внедрению и отладке Streaming Scan Network (SSN)? В чем основные преимущества этой архитектуры перед традиционным сканированием?
Критически важный навык для снижения себестоимости чипа.
Какие стратегии вы используете для оптимизации времени тестирования (test time reduction) без потери качества покрытия (coverage)?
Проверка навыков системного анализа и работы с данными.
Расскажите о случае, когда вам удалось значительно повысить выход годных кристаллов (yield) на основе анализа данных STDF. Каков был ваш алгоритм действий?
Оценка навыков автоматизации.
Какие процессы в жизненном цикле тестирования вы автоматизировали с помощью Python или TCL? Как это повлияло на производительность команды?
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