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Senior Software Engineering Lead, Resilience and Chaos Engineering
Исключительная возможность работать в дочерней компании Google (Alphabet) над передовыми технологиями на стыке ИИ и робототехники с высокой степенью ответственности.
Сложность вакансии
Высокая сложность обусловлена необходимостью глубоких знаний в Chaos Engineering, распределенных системах и робототехнике, а также управленческим опытом в глобальной компании уровня Google.
Анализ зарплаты
Зарплата для данной позиции Senior Lead в Сингапуре соответствует верхнему эшелону рынка BigTech, учитывая сложность домена робототехники.
Сопроводительное письмо
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Откликнитесь в intrinsicrobotics уже сейчас
Присоединяйтесь к Intrinsic, чтобы возглавить разработку систем устойчивости для нового поколения ИИ-робототехники в Сингапуре!
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
In this role, you will establish and lead an engineering team dedicated to the stability and endurance of our robotics software platform. You will design systems that proactively identify vulnerabilities within our APIs, SDKs, web interfaces, and cloud-to-edge communication layers. By simulating scenarios such as AI model inference timeouts, high network latency, data pipeline congestion, and malformed input, you will ensure the platform maintains a safe and predictable state even when the environment is not. You will guide a specialized team in developing automated frameworks that replicate real-world disruptions, thus providing a dependable infrastructure for the developers building the next generation of AI-driven robotics. This responsibility includes developing the necessary monitoring tools to gain deep insights into overall system health. As a key technical leader, you will collaborate with world-class engineering teams in Mountain View and Munich to synchronize resilience strategies and set global standards for software reliability.
How your work moves the mission forward
- Create automated resilience tests focusing on service boundaries and hybrid environments (on-prem and Cloud).
- Bolster the robustness of AI integrations by implementing failure injection within data pipelines.
- Deploy fuzzing and property-based testing techniques platform-wide to guarantee graceful degradation.
- Enhance the stability of developer tools and frontend systems against latency and service interruptions.
- Cultivate a culture of reliability through engineer mentorship in defensive programming and by spearheading global "Game Day" exercises.
- Construct observability tools to monitor and analyze holistic system health.
Skills you will need to be successful
- 4-year degree in Computer Science or equivalent professional experience.
- At least 5 years experience in software engineering.
- Demonstrable experience with cloud computing.
- Proven ability to lead a team, providing architectural guidance and fostering professional growth for other engineers in a global setting.
- Experience with one of Go, Python, or C++.
- Strong communication skills.
Skills that will differentiate your candidacy
- Distributed Systems Architecture: Strong experience building and debugging hybrid software environments where local runtimes interact with cloud-hosted services.
- Reliability Engineering Patterns: Deep understanding of software patterns for resilience, such as circuit breaking, retries with backoff, and bulkhead isolation.
- Automated Testing Proficiency: Expertise in creating frameworks for fault injection, property-based testing or coverage-guided fuzzing.
- Full-Stack Technical Knowledge: Competency in systems-level languages and an understanding of how to build resilient frontend interfaces.
- Infrastructure & Tooling: Hands-on experience with container orchestration (Kubernetes/Docker) and CI/CD pipelines to automate failure simulations.
- Robotics Software Experience: Hands-on experience with robotics frameworks or complex hardware-interfacing software.
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
- Kubernetes
- CI/CD
- Docker
- Observability
- Chaos Engineering
- Robotics
- Go
- Cloud Computing
- Distributed Systems Architecture
- Fault Injection
Возможные вопросы на собеседовании
Проверка понимания специфики Chaos Engineering в контексте робототехники.
Как бы вы спроектировали эксперимент по хаос-инжинирингу для системы, где задержка в облачном выводе ИИ-модели может привести к физическому столкновению робота?
Оценка опыта в обеспечении надежности распределенных систем.
Какие паттерны устойчивости (resilience patterns) вы считаете наиболее критичными для гибридных сред (on-prem + cloud) и почему?
Проверка лидерских качеств и умения работать в распределенной команде.
Опишите ваш опыт синхронизации технических стандартов между командами, находящимися в разных часовых поясах (например, Сингапур, Мюнхен и Калифорния).
Оценка навыков мониторинга и наблюдаемости.
Какие метрики вы бы внедрили в первую очередь для оценки 'здоровья' платформы, объединяющей аппаратное обеспечение и облачные сервисы?
Проверка технического кругозора в области тестирования.
В чем преимущество использования property-based тестирования по сравнению с обычными юнит-тестами для API робототехнической платформы?
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