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Senior Software Engineer, ML Ops & Infrastructure
Исключительная возможность работать в дочерней компании Google над передовыми задачами на стыке AI и робототехники в одном из главных технологических хабов Европы.
Сложность вакансии
Высокая сложность обусловлена необходимостью глубоких знаний как в MLOps (Kubernetes, GKE), так и в низкоуровневой разработке (C++, CUDA), а также спецификой робототехники.
Анализ зарплаты
Предлагаемая позиция Senior уровня в Мюнхене в компании такого масштаба обычно предполагает зарплату выше среднерыночной, дополненную значительным пакетом бонусов и акций (RSU). Мюнхен — один из самых дорогих городов Германии, что отражается в высоких зарплатных ожиданиях.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Software Engineer, ML Ops & Infrastructure position at Intrinsic. With a solid background in building scalable machine learning platforms and a deep proficiency in Python, C++, and Kubernetes, I am excited about the opportunity to contribute to a team that is redefining industrial robotics through AI. My experience in optimizing distributed training across large GPU clusters and managing complex model lifecycles aligns perfectly with Intrinsic's mission to make intelligent robotics accessible.
In my previous roles, I have successfully implemented GKE-based orchestration workflows and data pipelines that handle massive datasets, similar to the robotics data challenges described in your posting. I am particularly drawn to Intrinsic's unique position within the Google ecosystem and your focus on real-time robotic control. I am confident that my technical expertise in MLOps toolkits like Kubeflow and my passion for robotics will allow me to make immediate contributions to your infrastructure and the broader developer community.
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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 software engineer focused on MLOps and deep learning infrastructure, you will design and build the foundational systems that empower robots with advanced machine learning capabilities. You will work within a cross-functional team of engineers and researchers to develop infrastructure that streamlines the training, evaluation, and deployment of large-scale AI models. Your efforts will provide accessible tools for injecting machine learning techniques into the Intrinsic stack, managing compute resources across cloud and on-premise environments, and ensuring that complex model lifecycles are efficient, reliable, and scalable for real-world industrial applications.
How your work moves the mission forward
- Design and implement scalable infrastructure for training and deploying deep learning models on top of a real-time robotic control stack.
- Help optimize data loading and training speed across 1000+ GPU training jobs.
- Build data pipelines that support distributed computing to process large volumes of robotics data for model training.
- Develop APIs and tools that allow internal and external researchers to easily integrate machine learning techniques into their workflows. This could involve leading efforts to open source models and engage with the community.
- Optimize the allocation of compute resources, such as GPUs and TPUs, to reduce cost and latency during model development and create orchestration workflows to successfully run jobs on GKE.
- Create tools for model understanding and analysis to ensure reliability and traceability across the machine learning lifecycle.
Skills you will need to be successful
- Bachelor's degree in Computer Science, Robotics, Machine Learning, or a related technical field.
- 2 years of experience in software development with a focus on MLOps or machine learning infrastructure.
- Proficiency in programming with Python and C++.
- Experience with containerization and orchestration technologies, specifically Docker and Kubernetes.
- Experience with deep learning frameworks such as TensorFlow, JAX, or PyTorch.
- Experience with cloud computing platforms like Google Cloud Platform.
- Basic Front-end experience.
Skills that will differentiate your candidacy
- Master’s degree or PhD in Computer Science, Robotics, or a related field.
- Comprehensive knowledge of the entire image processing workflow, from initial sensor data acquisition (drivers, OS) up to the final application logic and decision-making.
- Experience with MLOps toolkits such as Kubeflow.
- Hands on experience with CUDA optimization or deep learning optimization.
- Experience deploying machine learning models at scale in production environments.
- Familiarity with robotics systems or industrial automation hardware.
- Practical experience debugging distributed systems and optimizing network performance.
- Experience with accelerator orchestration (e.g., XManager).
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++
- Docker
- Kubernetes
- TensorFlow
- JAX
- PyTorch
- Google Cloud Platform
- Google Kubernetes Engine
- Kubeflow
- CUDA
- Machine Learning
- MLOps
- Distributed Systems
- Robotics
Возможные вопросы на собеседовании
Проверка опыта работы с масштабируемой инфраструктурой, упомянутой в описании (1000+ GPU).
Расскажите о вашем опыте оптимизации распределенного обучения моделей. С какими узкими местами в производительности сети или хранилища вы сталкивались?
Вакансия требует навыков работы с GKE и оркестрацией ускорителей.
Как бы вы спроектировали систему оркестрации в GKE для эффективного распределения ресурсов GPU/TPU между несколькими исследовательскими командами?
Робототехника требует интеграции ML в системы реального времени.
Каков ваш подход к деплою тяжелых ML-моделей в системы с жесткими требованиями к задержке (latency) и реальному времени?
В описании упоминается работа с C++ и CUDA.
Опишите случай, когда вам приходилось оптимизировать CUDA-кернелы или использовать C++ для ускорения обработки данных в ML-пайплайне.
Intrinsic ценит вклад в open-source и работу с сообществом.
Как вы подходите к проектированию внутренних API, чтобы они были удобны не только для инженеров, но и для внешних исследователей или разработчиков?
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