Scale AI

Engineering Manager, Agent Oversight

Senior · Сан-Франциско, США · Английский B2

Не указано: формат работы

Навыки

  • AI-инструменты в работе
  • API (интеграции)
  • Отладка и поиск ошибок
  • LLM
  • Machine Learning
  • Мониторинг и observability
  • Retention и отток
Ещё 2
  • Роадмап
  • Смарт-контракты

О компании и продукте

  • Scale's mission is to develop reliable AI systems for the world's most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world's most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.
  • Applied Intelligence Systems is Scale's team focused on pushing the frontier of what agentic applications can do. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale enterprises and governments demand. We're growing fast, with increasing traction across both commercial and public sector customers, and we're just getting started — this team will define what dependable, production-grade agentic AI looks like.

Задачи

  • Lead a multi-disciplinary team of software and ML engineers to drive technical delivery across the Scale Generative AI Platform (SGP)
  • Own the platform's roadmap across deployment, monitoring, evaluation, and ML-driven improvement of agentic applications
  • Work cross-functionally with customers, forward deployed teams, product, and internal engineering teams to translate enterprise and government requirements into platform capabilities
  • Build and ship features end-to-end, from system design through debugging and testing
  • Drive high-velocity experimentation to validate and improve platform capabilities based on real customer usage
  • Establish the technical direction, culture, and processes for a fast-growing team
  • Mentor and develop both engineers and ML engineers/scientists, and influence how the team scales technically and organizationally

Требования

  • 7+ years of engineering experience, including 2+ years directly managing engineers or ML engineers responsible for a production ML/LLM-powered system — not just consuming a third-party ML API within a feature
  • Hands-on familiarity with agent architectures — tool use, planning, multi-agent orchestration — and the technical depth to make informed tradeoffs with your team
  • You can review ML experiment design or evaluation methodology well enough to ask sharp questions and earn credibility with ML engineers and scientists, even if you're not running the experiments yourself
  • Track record owning the full lifecycle of platform-level infrastructure — from initial design through scaling it across multiple internal or external teams as usage, headcount, and complexity grow
  • Experience collaborating with product managers, forward deployed engineering (FDE) teams, and customers to translate real-world requirements into prioritization decisions and shipped platform capabilities
  • Track record of building and growing high-performing engineering teams — including hiring, retention, or measurable improvements in team output or velocity

Будет плюсом

  • Experience building or overseeing evaluation, monitoring, or observability systems for ML/LLM-powered products in production
  • Strong grasp of the full ML/agent development lifecycle — from experimentation through production deployment and iteration
  • Deep understanding of modern LLMs and agentic system design, including prompt- and system-level optimization and integration with external tools, APIs, and services
  • Published research, open-source contributions, or patents in agentic systems, LLMs, or applied ML
  • Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints
  • 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
  • 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

Условия

  • 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 and may be inclusive of several career levels at Scale
  • 248,400 — $310,500 USD

Паспорт вакансии

История публикации

Появилась в Вакандии30 дней
Перепубликациинетпубликовалась один раз
Проверяли на источникеВидели 30 дней назад
Среди похожихНет данныху карточки не хватает полей, чтобы найти похожие

Откуда что взялось

Отмечено то, что вывели мы. Без пометки — значение назвал работодатель.

ГрейдSeniorвыведено из другого признака
Формат работыне указан
ГеографияСан-Франциско, СШАвычитано из текста вакансии
Зарплата≈ 21 703 USD в месяцнаша оценка, в вакансии не названа

Почему на этом месте в выдаче

Порядок выдачи объявлен контрактом: свежесть решает между днями, полнота и зарплата — внутри дня.

Полнота карточки753 из 4 полей: грейд, формат, география, зарплата
Зарплата названа0вилки работодателя нет, показана наша оценка

Проверка Вакандии

Источники и свежесть

Тип источника
Карьерный сайт работодателя
Найдено публикаций
1
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  • greenhouseОсновная публикация · 2025-10-31

Работодатель

Scale AI

50 активных вакансий · вилка работодателя указана в 17%

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Безопасность

Отклик уходит на сайт источника

Вакандия показывает вакансию, но не отправляет отклик и не проверяет работодателя. Сам отклик вы оставляете на внешнем сайтеjob-boards.greenhouse.io.

Признаки мошенничества
  • Просят предоплату, «залог» или деньги за обучение и оборудование.
  • Требуют код из SMS, данные банковской карты или доступ к «Госуслугам».
  • Быстро уводят в мессенджер и торопят с решением.
  • Обещают большой доход без опыта и без деталей задач.

Настоящий работодатель не просит денег и платёжных данных до трудоустройства.

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