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motional
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Сингапур
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Senior Software Engineer, ML Dev Enablement (AI Agents)

Оценка ИИ

Отличная вакансия для опытных инженеров, желающих работать на стыке Infrastructure и AI. Работа в известной компании (совместное предприятие Hyundai и Aptiv) над социально значимым продуктом с использованием самого современного стека технологий.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена необходимостью сочетать глубокие знания инфраструктуры (Kubernetes, AWS) с практическим опытом разработки AI-агентов (LangChain, LLM). Роль требует не только написания кода, но и архитектурного видения для трансформации процессов разработки в крупной компании.

Анализ зарплаты

Медиана110 000 $
Рынок85 000 $ – 140 000 $
Оценка ИИ

Зарплата для Senior-позиций в сфере ML Infrastructure в Сингапуре обычно выше среднего по рынку из-за дефицита специалистов на стыке DevOps и AI. Предложенный диапазон отражает высокие стандарты международных технологических компаний в регионе.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Software Engineer, ML Dev Enablement position at Motional. With over 5 years of experience in backend and infrastructure engineering, combined with a deep passion for Agentic AI, I am excited about the opportunity to evolve your Cloud Development Environment into a proactive, agent-driven ecosystem. My background in architecting scalable systems on Kubernetes and AWS aligns perfectly with your mission to empower ML researchers through frictionless development.

In my previous roles, I have successfully implemented LLM-powered tools using frameworks like LangChain to automate complex workflows, which mirrors your goal of building agents for autonomous debugging and resource optimization. I am particularly drawn to Motional's unique position as a leader in autonomous vehicle technology and your commitment to engineering excellence. I am confident that my expertise in Python, distributed systems, and ML orchestration frameworks like Ray will allow me to make immediate contributions to your Dev Enablement team.

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Откликнитесь в motional уже сейчас

Присоединяйтесь к Motional, чтобы создавать будущее автономного вождения с помощью передовых AI-агентов!

Описание вакансии

About the Role

We are looking for a Senior Software Engineer to join our ML Infrastructure: Dev Enablement team. Our mission is to build a frictionless development environment that empowers our researchers and engineers to innovate on deep learning models for autonomous driving.

We manage a high-scale Cloud Development Environment (CDE) platform that provides standardized, high-performance workspaces for ML development. In this role, your impact will be two-fold:

  1. Platform Ownership: You will act as a key owner of our CDE platform, ensuring its scalability, reliability, and seamless integration into the ML workflow.
  2. Agentic Evolution: You will lead our shift toward Agentic ML Workflows. You won't just be building static tools; you’ll be architecting AI Agents that act as force multipliers—helping engineers automate debugging, optimize resource usage, and accelerate the journey from code to a trained model.

What You’ll Be Doing

  • Scale & Evolve the Dev Platform: Lead engineering efforts to support and enhance our existing CDE platform, ensuring it meets the rigorous demands of large-scale ML experimentation.
  • Architect AI Agents: Design and implement LLM-powered agents capable of navigating the ML lifecycle—from automated code suggestions and log analysis to autonomous debugging of distributed training jobs.
  • Infrastructure Integration: Bridge the gap between AI agents and our core infra, ensuring agents can safely and effectively interact with Kubernetes, Ray, and AWS resources.
  • Collaborative Automation: Partner with ML Engineers to identify "productivity killers" and build agentic solutions (e.g., an agent that suggests fixes for common PyTorch distributed training errors).
  • Champion Engineering Excellence: Bring software engineering rigor to the "wild west" of LLM development, including building evaluation frameworks for agent performance, reliability, and security.
  • Mentor & Lead: Act as a subject matter expert on Agentic AI within the infrastructure team, guiding junior engineers and influencing our long-term technical roadmap.

What We’re Looking For

  • Experience: 5+ years of professional software engineering experience, with a focus on backend systems, distributed systems, or infrastructure.
  • Agentic AI Proficiency: Hands-on experience building applications with LLM frameworks (e.g., LangChain, LangGraph, or LlamaIndex). You understand how to turn a prompt into a reliable, tool-calling agent.
  • Technical Stack: Expert-level Python or Go. Deep experience with Kubernetes and Cloud is required.
  • Cloud Infrastructure: Proven experience with AWS (or similar)
  • Communication: Ability to translate complex infrastructure challenges into clear technical designs and collaborate across diverse engineering and research teams.

Bonus Points

  • ML Ecosystem: Experience with ML orchestration and training frameworks like Ray or PyTorch.
  • Remote Dev Expertise: Familiarity with Coder or other Cloud Development Environments (CDEs) at scale.
  • Experiences with managing or working with high-performance compute resources (GPUs).

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, LinkedInInstagram 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

  • Python
  • Go
  • Kubernetes
  • AWS
  • LangChain
  • LangGraph
  • LlamaIndex
  • Ray
  • PyTorch
  • Distributed Systems
  • LLM

Возможные вопросы на собеседовании

Проверка практического опыта работы с LLM-фреймворками, указанными в требованиях.

Расскажите о наиболее сложном AI-агенте, которого вы разработали. С какими проблемами надежности и вызова инструментов (tool-calling) вы столкнулись?

Вакансия предполагает интеграцию агентов с K8s и Ray.

Как бы вы спроектировали систему безопасности для AI-агента, имеющего доступ к управлению ресурсами в кластере Kubernetes?

Роль направлена на повышение продуктивности ML-инженеров.

Как вы определяете метрики успеха для инструментов Dev Enablement? Как понять, что внедрение AI-агентов действительно ускорило цикл разработки?

Требуется опыт работы с распределенными системами.

Опишите ваш опыт отладки распределенного обучения (например, в PyTorch или Ray). Какие паттерны ошибок чаще всего поддаются автоматизации?

Проверка навыков проектирования масштабируемых платформ.

С какими ограничениями масштабируемости вы сталкивались при работе с Cloud Development Environments (CDE) и как их решали?

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