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Research Engineer

Оценка ИИ

Исключительная возможность работать в авангарде ИИ-технологий в компании, принадлежащей SoftBank. Высокий престиж, работа над фундаментальными проблемами и отличный социальный пакет делают вакансию крайне привлекательной.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена необходимостью сочетать глубокие знания в области AI-исследований с навыками низкоуровневого программирования (C++/CUDA) и пониманием архитектуры железа. Требуется опыт работы с современными фреймворками и, желательно, наличие научных публикаций.

Анализ зарплаты

Медиана85 000 £
Рынок65 000 £ – 110 000 £
Оценка ИИ

Предлагаемая роль Research Engineer в Великобритании в сфере AI/Hardware обычно оплачивается выше среднего по рынку из-за дефицита специалистов на стыке ML и системного программирования. Указанные рыночные оценки соответствуют уровню компенсации в ведущих технологических хабах, таких как Кембридж и Лондон.

Сопроводительное письмо

I am writing to express my strong interest in the Research Engineer position at Graphcore. With a solid background in deep learning and a passion for hardware-aware algorithm development, I have closely followed Graphcore's innovations in the AI compute stack. My experience in optimizing machine learning models using PyTorch and JAX, combined with lower-level programming skills in C++ and Triton, aligns perfectly with your mission to push the boundaries of hardware efficiency.

In my previous work, I have focused on bridging the gap between high-level AI research and practical hardware implementation, specifically in areas like kernel fusion and performance optimization. I am particularly drawn to Graphcore Research's collaborative environment and your commitment to publishing at top-tier conferences like NeurIPS and ICML. I am eager to contribute my technical expertise to help define and test the next generation of AI hardware within the SoftBank ecosystem.

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Присоединяйтесь к команде Graphcore и создавайте будущее ИИ-вычислений вместе с экспертами SoftBank Group!

Описание вакансии

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary

As a research engineer at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. We are therefore looking for individuals who combine strong machine learning experience with practical engineering skills to deliver impactful AI research. We are seeking AI researchers with strong software engineering experience, particularly in lower-level programming and performance optimisation for hardware efficiency. Our research spans a broad range of topics, including efficient training and inference, world models, life sciences, reinforcement learning, and beyond. You will work closely with researchers to generate ideas and translate them into scalable implementations, contributing to publications and projects that help to steer the future of AI hardware.

The Team

Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world.

We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations.

Perhaps the best way to get an idea of what we’re all about is to read one of our papers or an article on our blog. If you’re excited to work at the cutting edge of AI supported by new hardware and want to develop your skills in this area, we’d love to hear from you!

Responsibilities and Duties

  • Generate AI/ML ideas, design experiments, implement them & evaluate results.
  • Prepare, submit & present your work to AI conferences and workshops.
  • Provide technical insight to internal teams by designing experiments and delivering clear, actionable reports.
  • Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware.

About you:

Essential:

  • Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry).
  • Python programming in a modern deep learning framework, e.g. PyTorch or JAX.
  • Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling.
  • Capable of designing, executing and reporting from ML experiments.
  • Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton.
  • Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling.
  • Practical familiarity with software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution

Desirable:

  • Mathematics skills to support the above: calculus, probability theory and linear algebra.
  • Experience submitting papers to international scientific conferences or workshops.

Benefits

In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications

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Навыки

  • Python
  • PyTorch
  • JAX
  • C++
  • CUDA
  • Triton
  • Deep Learning
  • Machine Learning Research
  • Performance Optimization
  • XLA
  • Linear Algebra
  • Calculus

Возможные вопросы на собеседовании

Проверка понимания связи между софтом и железом, что критично для Graphcore.

Как бы вы оптимизировали кастомный оператор в PyTorch для эффективного использования кэша и векторных движков процессора?

Оценка опыта кандидата в проведении полноценных исследований.

Расскажите о самом сложном эксперименте в области глубокого обучения, который вы разработали: от гипотезы до анализа результатов.

Проверка навыков работы с низкоуровневыми инструментами оптимизации.

В каких случаях вы предпочтете использовать Triton вместо написания сырых CUDA-кернелов, и какие ограничения это накладывает?

Оценка теоретической базы в области масштабирования моделей.

Какие основные узкие места возникают при распределенном обучении моделей с миллиардами параметров, и как их можно нивелировать на уровне софта?

Проверка способности работать в междисциплинарной команде.

Как вы подходите к коммуникации результатов своих исследований инженерам по аппаратному обеспечению для улучшения будущих архитектур?

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