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Ускорим процесс поиска работы
SeniorУдалённоПолная занятость

Sr. ML Ops Engineer, tvScientific

Оценка ИИ

Высокая оценка обусловлена престижем бренда Pinterest, очень конкурентной заработной платой и возможностью работать над сложными технологическими задачами в сфере CTV. Удаленный формат работы по США добавляет привлекательности.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует глубоких знаний Linux, системного мышления и опыта работы с высоконагруженными системами (RTB/HFT). Высокая планка обусловлена необходимостью менторства и работы с низкоуровневыми технологиями вроде NixOS или Zig.

Анализ зарплаты

Медиана195 000 $
Рынок160 000 $ – 250 000 $
Оценка ИИ

Предлагаемый диапазон ($155k - $320k) значительно выше среднего по рынку США для Senior MLOps ролей. Верхняя граница соответствует уровню топовых технологических компаний (Big Tech).

Сопроводительное письмо

I am writing to express my strong interest in the Senior MLOps Engineer position at tvScientific. With a robust background in high-performance software and a systems-oriented mindset, I am excited about the opportunity to scale the machine learning infrastructure for your Connected TV ad-buying platform. My experience in building reliable CI/CD pipelines and managing Kubernetes deployments aligns perfectly with your mission to automate and optimize TV advertising at scale.

Throughout my career, I have focused on improving developer experiences and upgrading observability tooling, which are key responsibilities for this role. I am particularly drawn to tvScientific's unique position within Pinterest and your focus on real-time bidding environments. I am confident that my technical leadership skills and expertise in Linux and distributed systems will allow me to make an immediate impact on your engineering team and help ensure smooth, sustainable growth for your AI initiatives.

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Составьте идеальное письмо к вакансии с ИИ-агентом

Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в pinterest уже сейчас

Присоединяйтесь к команде Pinterest и tvScientific, чтобы масштабировать будущее рекламы на ТВ с помощью передовых технологий MLOps!

Описание вакансии

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

About tvScientific

tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.

tvScientific is looking for a Senior MLOps Engineer! You'll be working with a distributed engineering team on our Connected TV ad-buying platform, as we scale our Machine Learning practice. We’ve cracked the code on optimizing TV ad campaigns. We’re scaling massively and we need your help to make that scale sustainable.

An Idealab company, tvScientific was co-founded by executives with deep roots in programmatic advertising and digital media. tvScientific helps our clients buy ads across the CTV universe, from Hulu to PlutoTV to the ad-supported tier of Disney+ and (HBO) Max. Since our acquisition by Pinterest, we're expanding our work on CTV to lift search & social advertising performance.

What you'll do:

  • Scale the decisionmaking process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments
  • Improve the developer experience for the data science team
  • Upgrade our observability tooling
  • Serve as a technical lead and mentor to the team
  • Make every deployment smooth as our infrastructure evolves.

What we're looking for:

  • Deep understanding of Linux
  • Excellent writing skills
  • A systems-oriented mindset
  • Experience in high-performance software (RTB, HFT, etc.)
  • Software engineering experience + reliability (e.g. CI/CD) expertise
  • Strong observability instincts
  • Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
  • Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
  • High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables
  • Nice-To-Haves:

+ Reverse-engineering experience

+ Terraform, EKS, or MLOps experience

+ Python, Scala, or Zig experience

+ NixOS experience

+ Adtech or CTV experience

+ Experience deploying a distributed system across multiple clouds

+ Experience in hard real-time low-latency (<10 ms) environments

In-Office Requirement Statement:

  • We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.

Relocation Statement:

  • This position is not eligible for relocation assistance. Visit ourPinFlex page to learn more about our working model.

#LI-SM4

#LI-REMOTE

At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.

US based applicants only

$155,584—$320,320 USD

Our Commitment to Inclusion:

Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.

+400% к собеседованиям

Создайте идеальное резюме с помощью ИИ-агента

Создайте идеальное резюме с помощью ИИ-агента

Навыки

  • Linux
  • Kubernetes
  • CI/CD
  • Terraform
  • Amazon EKS
  • Python
  • Scala
  • Zig
  • NixOS
  • Machine Learning
  • Distributed Systems
  • Observability

Возможные вопросы на собеседовании

Вакансия упоминает RTB и HFT, где задержки критичны. Важно понять, как кандидат оптимизирует ML-модели для таких условий.

Как бы вы спроектировали инфраструктуру для деплоя ML-моделей в среде с жестким реальным временем и задержкой менее 10 мс?

В списке 'Nice-To-Haves' указан NixOS, что говорит о стремлении к воспроизводимости инфраструктуры.

Каков ваш опыт работы с декларативным управлением конфигурациями (например, NixOS или Terraform) для обеспечения идентичности сред разработки и продакшена?

Одна из задач — улучшение опыта разработчиков (DevEx) для команды Data Science.

Какие инструменты или подходы вы использовали для автоматизации рабочих процессов дата-сайентистов, чтобы сократить время от идеи до деплоя модели?

Pinterest делает акцент на использовании ИИ в работе. Работодатель хочет видеть осознанный подход.

Расскажите, как вы интегрируете ИИ-инструменты в свой ежедневный рабочий процесс и как вы верифицируете результаты их работы, чтобы избежать ошибок?

Позиция подразумевает роль техлида и ментора.

Опишите случай, когда вам приходилось внедрять новые стандарты наблюдаемости (observability) в распределенной системе. Как вы убеждали команду следовать этим практикам?

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155 584 $ – 320 320 $