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Robotics Software Engineer, Robot Learning and Manipulation
Исключительная возможность работать в дочерней компании Google над передовыми технологиями ИИ и робототехники. Высокие требования компенсируются масштабом задач и инновационной средой.
Сложность вакансии
Высокая сложность обусловлена требованием степени PhD (или эквивалентного опыта), глубоких знаний в области RL и робототехники, а также необходимости работать напрямую с «железом» и сложными C++/Python стеками.
Анализ зарплаты
Зарплата для данной роли в Сингапуре соответствует топовому сегменту технологического рынка. Учитывая принадлежность к экосистеме Google, общая компенсация (TC), включая бонусы и акции, обычно значительно выше средних рыночных показателей для инженеров.
Сопроводительное письмо
I am writing to express my strong interest in the Robotics Software Engineer position at Intrinsic. With a solid background in robot learning and contact-rich manipulation, I am excited about the opportunity to contribute to your mission of making industrial robotics more intelligent and accessible. My experience in developing sensor-based control strategies and managing the full ML model lifecycle aligns perfectly with the requirements of this role.
In my previous work, I have successfully implemented reinforcement learning and imitation learning models for complex assembly tasks, utilizing frameworks like JAX and PyTorch. I am particularly drawn to Intrinsic's focus on real-world manufacturing applications and the challenge of optimizing learned models for real-time execution. I am eager to bring my expertise in multi-modal perception and my 'operational grit' to your lab in Singapore to help push the boundaries of what's possible in industrial automation.
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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 Robotics Software Engineer specializing in Robot Learning and Manipulation, you will drive the design, training, validation and deployment of contact-rich manipulation skills for real-world manufacturing. Your focus will be on solving dexterous, contact-rich assembly tasks. You will develop and integrate advanced sensor-based control strategies and Machine Learning (ML) policies within the Intrinsic Robot Control stack, managing the full model lifecycle - from architecture, large-scale data collection and training to validation, deployment and cycle-time optimization - to meet rigorous industrial standards.
How your work moves the mission forward
- Develop, train, and deploy multi-modal feedback- and interaction controllers to solve high-precision insertion and assembly tasks.
- Lead the AI manipulation model lifecycle, conducting regular trials on industrial hardware to evaluate algorithmic changes and curate high-quality training datasets.
- Architect and build modular components for reinforcement learning and imitation learning, continuously seeking to improve the robustness of contact-rich assembly tasks.
- Stress-test and optimize the real-time execution framework for learned manipulation models within the Intrinsic platform.
Skills you will need to be successful
- PhD or equivalent professional experience in Robot Learning and AI Manipulation (e.g., Applied RL, Visuomotor Policy Learning, Vision-Language-Action models).
- 2 years of professional experience in C++ and Python, with a proven track record of shipping production-quality code.
- ML Frameworks: Deep expertise with JAX, TensorFlow, or PyTorch.
- Hardware Experience: Direct experience testing and iterating on physical robots integrated with vision and force-torque sensors.
- Operational Grit: A desire to spend significant time in the lab, managing hardware experiments and troubleshooting physical-world edge cases.
- Communication: Business fluency in English.
Skills that will differentiate your candidacy
- Multimodal Perception and Control: Expertise in integrating tactile/force sensing and computer vision for contact-rich tasks.
- Hardware Experience: Direct experience testing and iterating on physical robot manipulators integrated with vision and force-torque sensors.
- Large-Scale Training: Proficiency in training on massive multimodal datasets and managing cloud-based training workflows (e.g. Argo).
- Infrastructure: Experience with cloud-based training (Argo) and deploying inference on the edge.
- Customer Focus: Willingness to travel internationally to analyze customer use cases and deploy solutions under ambitious timelines.
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
- C++
- Python
- JAX
- TensorFlow
- PyTorch
- Reinforcement Learning
- Imitation Learning
- Computer Vision
- Robotics
- Machine Learning
- Argo
Возможные вопросы на собеседовании
Проверка понимания специфики контактных задач в робототехнике.
Как бы вы подошли к решению проблемы дрейфа датчиков силы-момента при выполнении высокоточных операций сборки?
Оценка опыта работы с современными ML-фреймворками в контексте робототехники.
В чем преимущества использования JAX перед PyTorch при разработке контроллеров для роботов с низким временем задержки?
Проверка навыков работы с данными для обучения роботов.
Опишите ваш опыт создания пайплайнов для сбора и очистки данных с физических роботов для обучения имитационным моделям (Imitation Learning).
Оценка способности оптимизировать модели для реального времени.
Какие методы квантования или оптимизации инференса вы применяли для запуска тяжелых нейросетевых политик на Edge-устройствах?
Проверка готовности к практической работе в лаборатории.
Расскажите о самом сложном случае «необъяснимого» поведения робота в вашей практике и о том, как вы его отладили.
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