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Intern - Research
Исключительная возможность для исследователей поработать в компании-единороге под эгидой SoftBank. Высокий балл за возможность публикаций в топовых конференциях (NeurIPS, ICML) и доступ к передовым вычислительным мощностям.
Сложность вакансии
Высокая сложность обусловлена требованиями к академическому бэкграунду (PhD или сильная магистратура) и необходимостью наличия публикаций в области ML. Кандидат должен обладать как глубокими теоретическими знаниями, так и практическими навыками программирования на C++ и Python.
Анализ зарплаты
Зарплата для исследовательских интернов в британских ИИ-лабораториях (таких как Graphcore или DeepMind) обычно выше среднего по рынку стажировок и часто включает компенсацию жилья или релокации. Указанный диапазон отражает рыночные стандарты для PhD-уровня в Лондоне и Кембридже.
Сопроводительное письмо
I am writing to express my strong interest in the Research Intern position at Graphcore. As a PhD student specializing in Machine Learning, I have closely followed Graphcore's innovations in AI hardware and software co-design. My research background, particularly in optimizing model efficiency and implementing complex algorithms in PyTorch, aligns perfectly with your team's mission to push the boundaries of accelerated computation.
In my recent projects, I have focused on developing hardware-aware AI algorithms, which has given me a deep appreciation for the synergy between silicon architecture and model performance. I am particularly drawn to Graphcore's collaborative research environment and your track record of publishing at top-tier conferences like NeurIPS and ICML. I am eager to bring my technical skills in Python and C++ to your team and contribute to impactful research that leverages the IPU's unique capabilities.
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Откликнитесь в graphcore уже сейчас
Присоединяйтесь к команде Graphcore и внесите свой вклад в будущее ИИ-вычислений вместе с экспертами SoftBank Group!
Описание вакансии
About us
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 intern at Graphcore, you will advance AI research and investigate new ideas that push the limits on important AI/ML problems. Specialised hardware has driven progress in AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to this exciting field. We’re looking for candidates who are keen scientists and engineers, with the theoretical and practical skills needed for impactful AI research.
During your internship, you’ll investigate a research question that relates to machine learning or accelerated computation. Practically, this means designing experiments, implementing models and algorithms, exploring data and presenting results, with the aim to present work publicly to the wider research community. You will be largely self-directed in your research activity, with freedom to pursue the most promising approach, and with the benefit of appropriate mentoring and guidance.
Our team is interested in numerous topics across the field, including (but not limited to) efficient training and inference, improved model capabilities in the areas of factuality and reasoning, image and video generation, world models and molecule generation for life science applications.
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.
Candidate profile
We’re looking for technically-minded, highly-motivated and adaptable problem-solvers who can thrive in our collaborative team.
Essential
- A PhD student working in the field of Machine Learning and Artificial Intelligence, or be pursuing a Masters degree in a relevant field with demonstrable research experience (for example, interesting ML publications);
- Able to demonstrate your coding skills (Python, C++), for example through projects or previous work experience, and have experience of algorithm implementation in machine learning frameworks such as PyTorch;
- Passionate about the field of AI, both theory and practice, with a keen interest to continue learning.
We are excited to hear about your research! Please provide a short summary of a recent project with your application (and we’d love to hear about any other relevant work).
Benefits
In addition to a competitive salary, our centrally located offices provide a well-stocked kitchen with healthy food, drinks and snacks and our very own barista (Bristol only for now, but we have great coffee everywhere). We have an active social scene too: cycling, yoga, running, board games, table tennis, football, to name a few.
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.
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Навыки
- Python
- C++
- PyTorch
- Machine Learning
- Artificial Intelligence
- Algorithms
- Research
- Deep Learning
Возможные вопросы на собеседовании
Проверка глубины понимания собственных исследований и способности донести их ценность.
Расскажите о вашем самом значимом исследовательском проекте в области ML: какую проблему вы решали и каков был ваш личный вклад?
Graphcore специализируется на ускорителях, поэтому важно понимать связь софта и железа.
Как архитектурные особенности специализированного оборудования (например, IPU) могут повлиять на дизайн алгоритмов машинного обучения?
Проверка практических навыков работы с фреймворками.
Опишите ваш опыт оптимизации производительности моделей в PyTorch. С какими узкими местами вы сталкивались?
Оценка навыков низкоуровневого программирования, критических для разработки под AI-чипы.
В каких ситуациях при разработке ML-систем вы бы предпочли использовать C++ вместо Python? Приведите примеры из практики.
Проверка осведомленности о современных трендах и умения выбирать перспективные направления.
Какое направление в современных исследованиях ИИ (например, world models или эффективное обучение) кажется вам наиболее перспективным для аппаратного ускорения и почему?
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