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Senior Research Scientist, Robotic Foundation Models
Исключительная вакансия в дочерней компании Alphabet (Google) с возможностью работать на переднем крае науки. Мюнхен — отличная локация для R&D, а работа над Foundation Models сейчас является самым перспективным направлением в ИИ.
Сложность вакансии
Высокая сложность обусловлена требованием степени PhD, глубокой экспертизы в узких областях (Reinforcement Learning, VLA-модели) и опыта работы с JAX/C++. Роль предполагает лидерство в разработке архитектур мирового уровня.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции Senior Research Scientist в Мюнхене в компаниях уровня Alphabet (Intrinsic) вознаграждение обычно находится на верхней границе рынка, часто включая значительные бонусы и акции. Наш прогноз базируется на данных для ведущих ИИ-лабораторий Германии.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Research Scientist position for Robotic Foundation Models at Intrinsic. With a PhD specializing in Machine Learning and extensive experience in offline Reinforcement Learning and Imitation Learning, I have closely followed Intrinsic’s mission to democratize industrial robotics. My background in developing scalable ML architectures using JAX and C++ aligns perfectly with your goal of building robust foundation models that bridge the gap between simulation and real-world deployment.
In my previous research and engineering roles, I have successfully led projects focused on Vision-Language-Action models and high-performance robotic applications. I am particularly drawn to Intrinsic’s unique position within the Alphabet ecosystem, combining the agility of a robotics startup with the world-class infrastructure of Google. I am eager to contribute my expertise in large-scale ML deployment and service-oriented architectures to help empower developers and businesses worldwide through intelligent robotics.
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Откликнитесь в intrinsicrobotics уже сейчас
Присоединяйтесь к команде Intrinsic и создавайте будущее промышленной робототехники на базе ИИ в Мюнхене!
Описание вакансии
Intrinsic is an AI robotics group at Google aiming to reimagine the potential of industrial robotics. Our team believes that advances in AI, perception and simulation will redefine what’s possible for industrial robotics in the near future – with software and data at the core.
Our mission is to make industrial robotics intelligent, accessible, and usable for millions more businesses, entrepreneurs, and developers. We are a dynamic team of engineers, roboticists, designers, and technologists who are passionate about unlocking the creative and economic potential of industrial robotics.
Role
As a Senior Research Scientist focused on Robotic Foundation Models, you will be responsible for the design and implementation of systems that enable the training, deployment, and utilization of large-scale AI models for robotics. You will guide technical direction, mentor engineers, and ensure the development of robust, scalable ML models required to make robots significantly more capable in real-world environments. Your work will empower both internal teams and external partners by providing accessible tools and infrastructure for injecting advanced machine learning techniques into robotic applications.
How your work moves the mission forward
- Lead the design and implementation of infrastructure and architecture of large-scale robotic foundation models.
- Oversee the training and deployment of robust ML models for high-performance robotic applications.
- Empower ML researchers and engineers with accessible tools and infrastructure for developing and applying AI techniques in robotics.
- Collaborate with product and engineering teams to define infrastructure requirements and deliver successful solutions for partners and customers.
Skills you will need to be successful
- PhD in Computer Science / Engineering with a specialization in Machine Learning and / or Robotics.
- Experience collaborating on medium to large software projects for at least 4 years.
- Experience in programming Python / C++ and machine learning frameworks (Tensorflow, JAX).
- Demonstrable experience with offline Reinforcement / Imitation Learning in a robotics context.
- Proven ability to work with a team, setting directions and driving towards solutions.
Skills that will differentiate your candidacy
- Academic track record of publications in the field of offline Reinforcement / Imitation Learning in Robotics.
- Experience with Robotic Foundation Models / Vision-Language-Action Models.
- Experience with Online Reinforcement Learning and / or Computer Vision for Robotics.
- Experience with deploying ML services at scale, e.g., via cloud computing and containerization (Google Cloud, Docker, Kubernetes), databases (SQL, no-SQL), and service-oriented architecture frameworks.
At Intrinsic, we are proud to be an equal opportunity workplace. Employment at Intrinsic is based solely on a person's merit and qualifications directly related to professional competence. Intrinsic does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), or any other basis protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. It is Intrinsic’s policy to comply with all applicable national, state and local laws pertaining to nondiscrimination and equal opportunity.
If you have a disability or special need that requires accommodation, please contact us at: candidate-support@intrinsic.ai.
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Навыки
- Python
- C++
- TensorFlow
- JAX
- Reinforcement Learning
- Imitation Learning
- Computer Vision
- Google Cloud
- Docker
- Kubernetes
- SQL
- NoSQL
- Machine Learning
- Robotics
Возможные вопросы на собеседовании
Проверка глубины знаний в ключевой области, указанной в требованиях.
Какие основные проблемы вы видите при масштабировании offline Reinforcement Learning для реальных промышленных роботов и как вы их решали?
Оценка опыта работы с современными архитектурами, упомянутыми как преимущество.
Как бы вы спроектировали архитектуру Vision-Language-Action (VLA) модели для выполнения задач манипуляции в неструктурированной среде?
Проверка навыков работы с инструментарием Google/Intrinsic.
В чем, по вашему мнению, заключаются основные преимущества и недостатки использования JAX по сравнению с PyTorch при обучении крупномасштабных робототехнических моделей?
Оценка способности внедрять научные разработки в продакшн.
Опишите ваш опыт деплоя ML-сервисов в облачную инфраструктуру (Kubernetes/Docker). С какими проблемами задержки (latency) вы сталкивались в робототехнике?
Проверка лидерских качеств и умения направлять команду.
Как вы подходите к менторству инженеров и приоритизации исследовательских задач в условиях сжатых сроков разработки продукта?
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