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Senior Research Engineer, Data Engine
Исключительная вакансия в дочерней компании Google (Alphabet), предлагающая работу над передовыми технологиями на стыке AI и робототехники в одном из главных технологических хабов Европы.
Сложность вакансии
Высокая сложность обусловлена требованиями к глубоким знаниям в ML (JAX, TensorFlow), системном программировании (C++, Python) и инфраструктуре (Kubernetes, GCP), а также необходимостью опыта работы с робототехникой.
Анализ зарплаты
Зарплата для Senior-позиций в Мюнхене в сфере AI/Robotics обычно выше среднего по рынку, особенно в компаниях уровня Alphabet. Указанные рыночные оценки отражают стандарт для высококвалифицированных инженеров в Германии.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Research Engineer, Data Engine position at Intrinsic. With a solid background in developing scalable machine learning infrastructure and a deep passion for robotics, I am excited by Intrinsic's mission to make industrial robotics more intelligent and accessible. My experience in building robust data pipelines and deploying large-scale AI models aligns perfectly with your goal of redefining what is possible in the industrial sector.
In my previous roles, I have successfully led the design of ML workflows using Python, C++, and cloud technologies like Kubernetes and JAX. I am particularly drawn to this role because it combines the challenge of handling real-world robot data with the opportunity to mentor a talented team of engineers. I am confident that my technical expertise and collaborative approach will contribute significantly to the development of your robotic foundation models and the overall success of the Data Engine team.
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Откликнитесь в intrinsicrobotics уже сейчас
Присоединяйтесь к команде Intrinsic и создавайте будущее промышленной робототехники на базе технологий Google!
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 Engineer focused on Data Engine, 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 data pipelines and ML infrastructure 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 for training and deploying large-scale robotic foundation models.
- Oversee the building and maintenance of data pipelines for collecting, processing, and feeding real-world robot data into machine learning workflows.
- 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
- Four-year degree in Computer Science.
- Experience collaborating on medium to large software projects for at least 4 years.
- Experience in programming Python / C++.
- Experience with machine learning (Tensorflow, JAX), cloud computing and containerization (Google Cloud, Docker, Kubernetes), database (SQL, no-SQL), and service-oriented architecture frameworks.
- Proven ability to work with a team, setting directions and driving towards solutions.
Skills that will differentiate your candidacy
- PhD in Computer Science / Engineering with a specialization in Machine Learning and / or Robotics.
- Academic track record of publications in the field of Machine Learning and / or Robotics.
- Experience with Robotic Foundation Models / Vision-Language-Action Models.
- Experience with deploying ML services at scale.
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++
- Machine Learning
- TensorFlow
- JAX
- Google Cloud
- Docker
- Kubernetes
- SQL
- NoSQL
- Robotics
- Computer Vision
Возможные вопросы на собеседовании
Проверка опыта проектирования сложных систем для обработки данных.
Опишите архитектуру масштабируемого конвейера данных, который вы проектировали для обучения ML-моделей. С какими узкими местами вы столкнулись?
Оценка навыков работы с современными ML-фреймворками, используемыми в Google/Intrinsic.
В чем преимущества использования JAX по сравнению с другими фреймворками при разработке крупномасштабных моделей для робототехники?
Проверка понимания специфики данных в робототехнике.
Как вы подходите к проблеме обеспечения качества и консистентности данных, собираемых с различных физических роботов в реальных условиях?
Оценка лидерских качеств и умения направлять команду.
Расскажите о случае, когда вам нужно было принять сложное техническое решение, влияющее на несколько команд. Как вы аргументировали свой выбор?
Проверка опыта деплоя моделей.
Какие стратегии вы используете для мониторинга и обновления ML-сервисов в продакшене, чтобы минимизировать простои и ошибки?
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