- Страна
- США
- Зарплата
- 280 000 $ – 850 000 $
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TPU Kernel Engineer
Исключительная вакансия в одной из ведущих ИИ-лабораторий мира с очень высоким уровнем компенсации. Работа над социально значимыми и технически передовыми проектами в сильной команде.
Сложность вакансии
Роль требует редкого сочетания навыков: глубокого понимания архитектуры ускорителей (TPU), умения писать код на уровне ассемблера и знаний в области LLM. Высокая планка ответственности за производительность систем мирового уровня делает эту позицию крайне сложной.
Анализ зарплаты
Предлагаемый диапазон ($280k - $850k) значительно превышает средние рыночные показатели даже для Tier-1 компаний в США. Верхняя граница соответствует уровню Principal/Distinguished Engineer в BigTech.
Сопроводительное письмо
I am writing to express my strong interest in the TPU Kernel Engineer position at Anthropic. With a deep background in low-level optimization and accelerator architecture, I have consistently focused on pushing the boundaries of hardware performance for large-scale machine learning systems. My experience in designing custom kernels and debugging at the assembly level aligns perfectly with Anthropic's mission to build reliable and steerable AI.
In my previous work, I have successfully addressed performance bottlenecks in training and inference pipelines, often working closely with research teams to translate architectural changes into tangible throughput gains. I am particularly drawn to Anthropic’s collaborative 'big science' approach and your commitment to AI safety. I am eager to bring my expertise in TPU optimization and collective communication algorithms to help scale your transformer-based models efficiently.
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Откликнитесь в anthropic уже сейчас
Присоединяйтесь к Anthropic, чтобы оптимизировать будущее ИИ на самом низком уровне и работать с передовыми TPU-системами!
Описание вакансии
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the Role
As a TPU Kernel Engineer, you'll be responsible for identifying and addressing performance issues across many different ML systems, including research, training, and inference. A significant portion of this work will involve designing and optimizing kernels for the TPU. You will also provide feedback to researchers about how model changes impact performance. Strong candidates will have a track record of solving large-scale systems problems and low-level optimization.
You may be a good fit if you:
- Have significant experience optimizing ML systems for TPUs, GPUs, or other accelerators
- Are results-oriented, with a bias towards flexibility and impact
- Pick up slack, even if it goes outside your job description
- Enjoy pair programming (we love to pair!)
- Want to learn more about machine learning research
- Care about the societal impacts of your work
Strong candidates may also have experience with:
- High performance, large-scale ML systems
- Designing and implementing kernels for TPUs or other ML accelerators
- Understanding accelerators at a deep level, e.g. a background in computer architecture
- ML framework internals
- Language modeling with transformers
Representative projects:
- Implement low-latency, high-throughput sampling for large language models
- Adapt existing models for low-precision inference
- Build quantitative models of system performance
- Design and implement custom collective communication algorithms
- Debug kernel performance at the assembly level
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$280,000—$850,000 USD
Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- C++
- Large Language Models
- Transformers
- Computer Architecture
- Performance Engineering
- GPU
- Machine Learning Frameworks
- TPU
- Assembly Language
- Kernel Optimization
Возможные вопросы на собеседовании
Проверка понимания специфики работы с тензорными процессорами и управления памятью.
Каковы основные различия в оптимизации ядер для TPU по сравнению с GPU, особенно в контексте использования локальной памяти (HBM vs VMEM)?
Оценка навыков работы с низкоуровневым кодом и инструментами профилирования.
Опишите ваш процесс отладки производительности ядра на уровне ассемблера. Какие метрики для вас приоритетны?
Проверка опыта в распределенных вычислениях, критически важных для обучения LLM.
Как бы вы спроектировали кастомный алгоритм коллективной коммуникации для минимизации задержек при обучении модели на нескольких тысячах чипов?
Оценка способности работать на стыке железа и алгоритмов.
Как изменения в архитектуре трансформеров (например, переход к разреженному вниманию) влияют на стратегию оптимизации ядер TPU?
Проверка навыков оптимизации для инференса.
С какими основными трудностями вы сталкивались при реализации инференса с низкой точностью (low-precision) и как вы их решали?
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- Страна
- США
- Зарплата
- 280 000 $ – 850 000 $