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scaleai
Страна
США
Зарплата
252 000 $ – 315 000 $
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LeadВ офисеПолная занятость

Tech Lead Manager- MLRE, ML Systems

Оценка ИИ

Исключительная вакансия в одной из самых влиятельных AI-компаний мира. Высокая компенсация, работа с передовыми технологиями (LLM, RLHF) и возможность влиять на развитие всей индустрии ИИ делают это предложение топовым на рынке.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует редкого сочетания навыков управления командой (TLM) и глубокой технической экспертизы в оптимизации распределенного обучения LLM, CUDA и алгоритмов RLHF. Высокая планка ожиданий обусловлена работой на переднем крае технологий ИИ в одной из ведущих компаний индустрии.

Анализ зарплаты

Медиана280 000 $
Рынок240 000 $ – 350 000 $
Оценка ИИ

Предложенная зарплата в $252k–$315k находится на верхнем уровне рыночных значений для позиций Engineering Manager в Tier-1 компаниях США. С учетом бонусов и опционов (equity), совокупный доход может значительно превышать средние показатели по рынку.

Сопроводительное письмо

I am writing to express my strong interest in the Tech Lead Manager position for the MLRE and ML Systems team at Scale AI. With a deep background in optimizing distributed ML systems and a passion for the intricacies of LLM post-training, I have closely followed Scale's pivotal role in providing the high-quality data and infrastructure that powers today's leading models. My experience in building large-scale training frameworks and implementing algorithms like PPO and GRPO aligns perfectly with your mission to accelerate the development of reliable AI systems.

In my previous roles, I have successfully led teams through the complexities of multi-node training and inference optimization, utilizing tools like CUDA, PyTorch, and Flash Attention to achieve significant performance gains. I am particularly drawn to this role because it combines technical leadership with the opportunity to collaborate across research and engineering teams to solve fundamental innovations in the ML lifecycle. I am confident that my technical expertise and leadership style will contribute to Scale's continued success in shaping the future of AI.

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Составьте идеальное письмо к вакансии с ИИ-агентом

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Откликнитесь в scaleai уже сейчас

Присоединяйтесь к Scale AI, чтобы возглавить разработку систем обучения LLM нового поколения и определять будущее ИИ!

Описание вакансии

Scale's LLM post-training platform team builds our internal distributed framework for large language model training. The platform powers MLEs, researchers, data scientists, and operators for fast and automatic training and evaluation of LLMs. It also serves as the underlying training framework for the data quality evaluation pipeline.

Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimizing the platform to enable our next generation LLM training, inference and data curation.

If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you!

You will:

  • Build, profile and optimize our training and inference framework.
  • Collaborate with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.
  • Research and integrate state-of-the-art technologies to optimize our ML system.

Ideally you’d have:

  • Passionate about system optimization
  • Experience with multi-node LLM training and inference
  • Experience with developing large-scale distributed ML systems
  • Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
  • Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.
  • Strong written and verbal communication skills to operate in a cross functional team environment.

Nice to haves:

  • Demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:

$252,000—$315,000 USD

*PLEASE NOTE:*Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision.

PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

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Создайте идеальное резюме с помощью ИИ-агента

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Навыки

  • Python
  • PyTorch
  • Machine Learning
  • LLM
  • Transformers
  • Distributed Systems
  • CUDA
  • RLHF
  • PPO
  • Flash Attention

Возможные вопросы на собеседовании

Проверка опыта работы с инфраструктурой для обучения гигантских моделей.

Расскажите о самом сложном случае отладки производительности при распределенном обучении LLM на нескольких узлах. Какие инструменты профилирования вы использовали?

Оценка понимания современных методов дообучения моделей.

В чем заключаются основные инженерные сложности при реализации алгоритмов GRPO или PPO в масштабируемой среде по сравнению со стандартным SFT?

Проверка навыков оптимизации на низком уровне.

Как бы вы подошли к оптимизации использования памяти GPU при работе с Flash Attention и очень длинными контекстами?

Оценка лидерских качеств в гибридной роли.

Как вы балансируете между написанием кода/архитектурным проектированием и управленческими обязанностями (people management) в роли Tech Lead Manager?

Проверка способности работать с внутренними заказчиками (исследователями).

Опишите ваш опыт взаимодействия с командами ML Research. Как вы приоритизируете запросы на новые фичи в платформе обучения при ограниченных ресурсах?

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scaleai
Страна
США
Зарплата
252 000 $ – 315 000 $