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- США
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- 170 000 $ – 216 000 $
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Machine Learning Engineer
Исключительная вакансия в одной из ведущих компаний мира в сфере беспилотников. Предлагает работу с самыми современными технологиями (GenAI, World Models) и конкурентную заработную плату с отличным соцпакетом.
Сложность вакансии
Высокая сложность обусловлена требованиями к глубоким знаниям в области Generative AI и Foundation Models, а также необходимостью опыта работы с крупномасштабными кластерами GPU/TPU. Ожидается владение как Python, так и C++ на уровне продакшн-разработки.
Анализ зарплаты
Предлагаемый диапазон $170k–$216k является конкурентным для уровня Senior ML Engineer в Кремниевой долине, однако для топовых специалистов в области GenAI рыночные показатели могут достигать верхней границы и выше за счет бонусов и акций. Данная зарплата соответствует стандартам компаний уровня Tier-1 (Big Tech).
Сопроводительное письмо
I am writing to express my strong interest in the Machine Learning Engineer position within the Simulator Team at Waymo. With over five years of experience in developing complex ML models using PyTorch and JAX, and a deep fascination with autonomous driving technology, I am eager to contribute to your mission of building the world's most trusted driver. My background in scaling large-scale models on GPU clusters aligns perfectly with your requirements for developing foundational World Models.
In my previous roles, I have focused on bridging the gap between research and production, a skill I see as vital for this position. I am particularly excited about the opportunity to apply Generative AI and Reinforcement Learning to enhance simulation realism. I am confident that my technical expertise in Python and C++, combined with my passion for robotics, will allow me to make immediate and impactful contributions to the Waymo Driver's performance and safety.
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Откликнитесь в waymo уже сейчас
Присоединяйтесь к команде Waymo и создавайте будущее автономного вождения с помощью передовых технологий генеративного ИИ!
Описание вакансии
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.
The Simulator Team at Waymo builds state-of-the-art simulations of realistic environments for testing and training the Waymo Driver. Our team is a diverse and collaborative group of software engineers, machine learning (ML) engineers, and data scientists. We develop industry-leading simulation solutions using advanced ML algorithms that measure and enhance the performance of the Waymo Driver. By applying machine learning, we model the real world, including realistic agents (vehicles, pedestrians, cyclists, motorcyclists), roads, traffic control systems, and weather conditions.
To increase the fidelity and steerability of our simulations, we employ the latest ML technologies such as large foundational World Model trained on our datasets, world understanding and reasoning capabilities for tail cases, and reinforcement learning for long term credit assignment. We also invest in capable infrastructure that allows us to quickly set up and roll out multiple counterfactual scenarios to rigorously test our autonomous driving systems.
In this hybrid role, you will report to a Senior Staff Tech Lead Manager
You will:
- Drive innovation in ultra-realistic world simulation for autonomous vehicles. You will develop the latest simulation technologies using foundation models for embodied agents.
- Apply your deep Machine Learning (ML) expertise, in Generative AI (GenAI), to push the boundaries of simulation realism and directly influence the advancement of autonomous driving technology.
- Work with Waymo's foundational AI research team to transfer research into scalable and production-ready solutions.
You have:
- 5+ years of experience with ML software engineering
- 5+ years of experience with Python
- 5+ years of ML software experience with machine learning framework including Pytorch, Tensorflow, Jax/Flax ,
We prefer:
- Experience working with, creating and/or developing ML models for robotics and/or self driving vehicles
- Strong C++ in production setting, with experience in integrating ML models in production system
- Experience in training large scale models on GPU/TPU clusters are strongly preferred
#LI-Hybrid
The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.
Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.
Salary Range
$170,000—$216,000 USD
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- C++
- Python
- PyTorch
- Machine Learning
- JAX
- Computer Vision
- TensorFlow
- Generative AI
- Robotics
- Reinforcement Learning
- Flax
Возможные вопросы на собеседовании
Позиция предполагает работу с World Models для симуляции.
Как бы вы подошли к оценке реалистичности поведения агентов (автомобилей и пешеходов) в генеративной модели мира?
В описании упоминается обучение больших моделей на кластерах.
Опишите ваш опыт оптимизации распределенного обучения (Distributed Training) для моделей с миллиардами параметров.
Waymo ценит умение переносить исследования в продакшн.
Расскажите о случае, когда вам пришлось оптимизировать ML-модель для работы в системе с жесткими ограничениями по задержке (latency).
Упоминается использование Reinforcement Learning.
Как вы решаете проблему 'credit assignment' в долгосрочных сценариях симуляции автономного вождения?
Требуется опыт работы с C++ в продакшн-среде.
Каковы основные сложности интеграции моделей, обученных на Python/PyTorch, в высокопроизводительный C++ движок симуляции?
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- Страна
- США
- Зарплата
- 170 000 $ – 216 000 $