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Staff Software Engineer, Basemap (SLAM)
Исключительная возможность работать в лидирующей компании в сфере беспилотников (JV Hyundai и Aptiv) над сложнейшими технологическими задачами. Высокий престиж и работа с передовым стеком.
Сложность вакансии
Высокая сложность обусловлена требованиями к ученой степени (Master's/PhD), глубоким знаниям в узкой области SLAM и нелинейной оптимизации, а также статусом Staff-инженера.
Анализ зарплаты
Зарплата для Staff-позиций в сфере Robotics/AI в Сингапуре значительно выше среднего по рынку ПО и обычно включает существенные бонусы. Предложенный диапазон отражает высокие требования к квалификации.
Сопроводительное письмо
I am writing to express my strong interest in the Staff Software Engineer position within the Basemap team at Motional. With over five years of experience in state estimation and SLAM, combined with a deep proficiency in C++ and Python, I am eager to contribute to your mission of making autonomous vehicles a safe and accessible reality. My background in nonlinear optimization and numerical linear algebra aligns perfectly with the technical challenges described in your large-scale mapping projects.
Throughout my career, I have focused on developing scalable software for complex robotic systems. I am particularly drawn to Motional's collaborative culture and your track record of industry-firsts, such as the world's first robotaxi pilot. I am confident that my expertise in handling diverse sensor data and my passion for mentoring fellow engineers will allow me to make a significant impact on your Autonomy team in Singapore.
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Откликнитесь в motional уже сейчас
Присоединяйтесь к Motional, чтобы создавать будущее автономного транспорта в Сингапуре!
Описание вакансии
As part of our mapping team, you'll collaborate with world-class experts in mapping, software engineering, and machine learning to build cutting-edge, next-generation mapping systems. We’re leveraging the latest technologies to lead the industry in mapping innovation, and we’re seeking passionate engineers who are excited about mapping, autonomous vehicles, and tackling intellectual challenges. This is an exceptional opportunity to contribute to the growth of a company at the forefront of the autonomous vehicle revolution.
What You’ll Be Doing:
- Tackle complex challenges in large-scale mapping by applying state-of-the-art methods and technologies.
- Develop high-quality, scalable software using Python, C++, and other advanced technologies.
- Work with large-scale, diverse data from various sources, including vehicle sensors and geospatial data.
- Mentor and support software engineers and domain experts, fostering their professional growth.
- Collaborate with cross-functional teams, including both technical and non-technical stakeholders, on challenging and impactful projects.
What We Are Looking For:
- Master’s or PhD in Computer Science, Robotics, or a related field.
- 5+ years of experience in state estimation and SLAM
- Deep experience with C++ or Python
- Strong foundation in nonlinear optimization and numerical linear algebra.
- Excellent communication and collaboration skills to work across teams of researchers and engineers.
- A passion for problem-solving and a keen ability to learn and adapt.
Bonus Points (not required, but a plus):
- Experience applying machine learning techniques to mapping or geospatial problems.
- Familiarity with distributed computing and cloud systems.
Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more.
Our journey is always people first.
We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.
Higher purpose, greater impact.
We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.
Scale up, not starting up.
Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.
Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit www.Motional.com and follow us on Twitter, LinkedIn, Instagram and YouTube.
Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibility.
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Навыки
- C++
- Python
- SLAM
- Robotics
- Machine Learning
- Linear Algebra
- Optimization
- Distributed Computing
- Cloud Computing
- Geospatial Data
Возможные вопросы на собеседовании
Позиция требует глубоких знаний в SLAM.
Можете ли вы объяснить различия между фильтрационными (EKF) и оптимизационными (Graph-based) подходами в SLAM и в каких случаях предпочтительнее каждый из них?
Работа связана с крупномасштабным картографированием.
Как вы решаете проблему накопления дрейфа (drift) и замыкания цикла (loop closure) при построении карт больших территорий?
Упоминается работа с нелинейной оптимизацией.
Какие библиотеки для нелинейной оптимизации (например, Ceres Solver, g2o) вы использовали и как вы подходите к выбору функции потерь для минимизации влияния выбросов (outliers)?
Роль Staff-инженера подразумевает менторство.
Опишите случай, когда вам приходилось направлять архитектурное решение команды или менторить младших коллег в условиях сжатых сроков.
Важна работа с сенсорами.
Каков ваш опыт калибровки и синхронизации данных с различных датчиков (LiDAR, камеры, IMU) для задач локализации?
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