- Страна
- США
- Зарплата
- 189 721 $ – 332 012 $
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Sr. Machine Learning Engineer, Core Engineering
Высокая оценка обусловлена сильным брендом компании, прозрачным и конкурентным диапазоном зарплаты, а также возможностью работать с уникальными графовыми данными на огромном масштабе. Компания поощряет использование современных AI-инструментов.
Сложность вакансии
Роль требует глубоких знаний в области рекомендательных систем и опыта работы с Big Data (Hadoop/Spark). Высокий уровень сложности обусловлен масштабами данных Pinterest и необходимостью владения современными методами глубокого обучения.
Анализ зарплаты
Предлагаемая зарплата ($189k - $332k) находится на верхнем уровне рынка для Senior ML ролей в США, особенно учитывая дополнительные бонусы в виде акций (equity). Нижняя граница соответствует медиане для крупных технологических компаний, а верхняя значительно её превышает для индивидуальных контрибьюторов.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Machine Learning Engineer position at Pinterest. With over four years of experience in developing large-scale recommendation systems and deep learning models, I have consistently focused on leveraging data-driven methods to enhance user personalization and retrieval systems. My background in building end-to-end data pipelines using Spark and Hadoop aligns perfectly with the technical requirements of your Core Engineering team.
What excites me most about Pinterest is the unique opportunity to work with a massive vault of visual and intent data to solve complex ranking and recommendation challenges. I am particularly impressed by Pinterest's forward-thinking approach to integrating AI assistants like Cursor and Copilot into the engineering workflow, as I am a strong advocate for using LLM-powered tools to accelerate development and experiment analysis. I am eager to bring my expertise in scalable real-time systems to help Pinners discover ideas they love.
Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в pinterest уже сейчас
Присоединяйтесь к команде Pinterest и создавайте персонализированный опыт для 500 миллионов пользователей по всему миру!
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.
With more than 500 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 3,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.
What you’ll do:
- Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
- Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
- Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
- Work in a high-impact environment with quick experimentation and product launches
- Keeping up with industry trends in recommendation systems
What we’re looking for:
- 4+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
- End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
- Degree in computer science, machine learning, statistics, or related field
- Nice to have:
+ Publications at top ML conferences
+ Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring
+ Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration
+ Expertise in scalable realtime systems that process stream data
+ Passion for applied ML and the Pinterest product
+ MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences, related field, or equivalent experience.
Relocation Statement:
- This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
#LI-SA1
#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
$189,721—$332,012 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.
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Навыки
- Machine Learning
- Deep Learning
- Recommendation Systems
- Hadoop
- Spark
- NLP
- Reinforcement Learning
- Graph Representation Learning
- SQL
- Python
- Large Language Models
Возможные вопросы на собеседовании
Pinterest сильно завязан на рекомендациях. Важно понимать, как кандидат подходит к проблеме выбора релевантного контента из миллиардов пинов.
Как бы вы спроектировали систему многоэтапного отбора кандидатов (retrieval) для ленты рекомендаций при наличии сотен миллионов объектов?
Вакансия требует опыта работы с большими данными. Вопрос проверяет практические навыки оптимизации.
Опишите ваш опыт оптимизации Spark-пайплайнов для обучения ML-моделей на терабайтных выборках. С какими узкими местами вы сталкивались?
В описании упоминается использование AI-ассистентов. Компании важно, как инженер повышает свою продуктивность.
Как вы используете LLM или AI-кодинг ассистенты (например, Copilot или Cursor) в своем ежедневном рабочем процессе для отладки или рефакторинга?
Pinterest работает в реальном времени. Важно уметь балансировать между точностью модели и скоростью ответа.
Как вы подходите к тестированию и внедрению новых признаков (features) в ранжирование Ads или Homefeed без деградации задержки (latency) системы?
Для Senior-позиции критично умение анализировать результаты экспериментов.
Расскажите о случае, когда оффлайн-метрики модели улучшились, а онлайн-метрики (например, CTR или время сессии) упали. Как вы проводили диагностику?
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