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Software Engineer, Perception
Исключительная возможность работать в AI-подразделении Google над передовыми задачами робототехники. Вакансия предлагает работу с новейшим стеком технологий и высокий престиж компании, хотя и требует очень специфических навыков.
Сложность вакансии
Высокая сложность обусловлена требованиями к ученой степени (Master/PhD) и глубоким знаниям на стыке Deep Learning, C++ и робототехники. Работа в дочерней компании Google предполагает строгий отбор и высокие стандарты разработки.
Анализ зарплаты
Зарплата для Software Engineer в области AI/Robotics в Сингапуре обычно выше среднего по рынку ИТ. Учитывая принадлежность к Alphabet (Google), можно ожидать компенсацию по верхней границе рынка, включая значительные бонусы и акции.
Сопроводительное письмо
I am writing to express my strong interest in the Software Engineer, Perception position at Intrinsic. With a solid background in deep learning and 3D computer vision, I am eager to contribute to your mission of making industrial robotics more intelligent and accessible. My experience in developing and deploying object detection and 6DoF pose estimation models aligns perfectly with the core responsibilities of this role.
In my previous work, I have successfully implemented transformer-based models and optimized them for production environments using PyTorch and C++. I am particularly drawn to Intrinsic's innovative approach to combining AI with physical robotics, and I am confident that my skills in 3D reconstruction and robotic grasping will help move your mission forward. I look forward to the possibility of discussing how my technical expertise can support the Engineering team in Singapore.
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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 Deep Learning Engineer, you will apply your strong skills in software engineering, deep learning, 3D computer vision to implement, optimize and deploy the perception capabilities for industrial robots. You will be instrumental in developing and deploying advanced AI models for object detection, object pose estimation, 3D reconstruction, and grasping. Your work will enable robots to intelligently interact with their environment in real-world industrial settings, translating cutting-edge deep learning research into robust, deployable solutions that make industrial robotics more intelligent and accessible.
How your work moves the mission forward
- Develop and implement robust deep learning models for 3D computer vision tasks, including object detection, object pose estimation, and 3D reconstruction.
- Optimize and deploy algorithms for reliable grasping and manipulation in complex industrial robotics environments.
- Enhance robot perception capabilities, enabling more complex and reliable interactions in dynamic manufacturing settings.
- Contribute to the design, implementation and documentation of core perception software skills within Intrinsic's platform.
- Support users in applying perception skills in production applications.
Skills you will need to be successful
- A Master’s or PhD degree in Computer Science, Electrical Engineering, Robotics, or a closely related technical field.
- 1-3 years of experience in AI, machine learning, computer vision, or robotics, with a strong focus on deep learning, including transformer based models.
- Demonstrated experience with deploying computer vision in production, along with at least one of the following topics: object detection, 6dof pose estimation, or 3D reconstruction.
- Proficiency in at least one modern deep learning framework (e.g., PyTorch, TensorFlow, JAX).
- Strong programming skills in Python and C++.
Skills that will differentiate your candidacy
- Expertise in computer vision and robotics.
- Experience with GPU kernel programming in CUDA.
- Experience with optimizing or quantizing ML models for deployment.
- Experience developing algorithms for robotic perception, grasping and/or manipulation.
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
- Deep Learning
- Computer Vision
- 3D Reconstruction
- PyTorch
- TensorFlow
- JAX
- Python
- C++
- CUDA
- Object Detection
- Pose Estimation
- Robotics
- Machine Learning
Возможные вопросы на собеседовании
Проверка фундаментальных знаний в области 3D зрения, критически важных для задач манипуляции.
Как вы решаете проблему неоднозначности при оценке 6DoF позы симметричных объектов?
Оценка навыков оптимизации моделей для работы в реальном времени на роботах.
Какие методы квантования и оптимизации ML-моделей вы использовали для деплоя на GPU с использованием CUDA или TensorRT?
Проверка опыта работы с современными архитектурами, указанными в вакансии.
В чем преимущество использования Transformer-based моделей в задачах 3D реконструкции по сравнению с классическими CNN?
Оценка практического опыта интеграции алгоритмов в физические системы.
Расскажите о вашем опыте калибровки системы 'камера-робот' и как ошибки калибровки влияли на точность захвата (grasping).
Проверка навыков написания высокопроизводительного кода.
В каких случаях при разработке систем восприятия вы предпочтете C++ вместо Python, и как вы организуете взаимодействие между ними?
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