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ГибридПолная занятость

Staff Machine Learning Engineer, Taxonomy

Оценка ИИ

Высокий балл обусловлен сильным брендом компании, социально значимой миссией и возможностью стать первым профильным инженером по таксономии, что дает большое влияние на продукт. Предлагается отличный пакет бенефитов и гибридный формат работы в технологических хабах США.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль уровня Staff требует не только глубоких технических знаний в NLP и классификации контента, но и лидерских качеств для работы с кросс-функциональными командами. Ожидается опыт работы с высоконагруженными системами и неструктурированными данными более 8 лет.

Анализ зарплаты

Медиана260 000 $
Рынок225 000 $ – 310 000 $
Оценка ИИ

Patreon предлагает конкурентоспособную зарплату для уровня Staff в Сан-Франциско и Нью-Йорке. Хотя точные цифры в вакансии не указаны, рыночные оценки для таких позиций в Tier-1 компаниях начинаются от $220,000 и могут достигать $300,000+ без учета опционов.

Сопроводительное письмо

I am writing to express my strong interest in the Staff Machine Learning Engineer, Taxonomy position at Patreon. With over 8 years of experience in applied machine learning and a deep focus on NLP and content classification, I am excited by the opportunity to build the foundational taxonomy systems that will power discovery and relevance for your 300,000+ creators.

In my previous roles, I have successfully deployed end-to-end ML pipelines that handle unstructured data at scale, including text and multimedia content. I have extensive experience in topic modeling and clustering, which aligns perfectly with your goal of creating sophisticated creator and content profiles. I am particularly drawn to Patreon's mission of funding the creative class and would welcome the chance to apply my technical leadership to help fans and creators connect more effectively.

I am proficient in Python and common ML libraries, and I have a proven track record of collaborating with cross-functional teams to translate complex data into actionable product insights. I look forward to the possibility of discussing how my background in building robust classification systems can contribute to the Relevance team's success.

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Присоединяйтесь к Patreon и помогите миллионам креаторов найти свою аудиторию с помощью передовых ML-технологий!

Описание вакансии

Patreon is a media and community platform where over 300,000 creators give their biggest fans access to exclusive work and experiences. We offer creators a variety of ways to engage with their communities and build a lasting business including: paid memberships, free memberships, community chats, live experiences, and selling to fans directly with one-time purchases.

Ultimately our goal is simple: fund the creative class. And we're leaders in that space, with:

  • $8 billion+ in revenue generated since Patreon's inception
  • 60 million+ free new memberships for fans who may not be ready to pay just yet, and
  • 10 million+ fans paying each month for exclusive access to creators' work and community.

As we scale the platform, understanding what our creators are making and what fans are consuming becomes increasingly important. We’re looking for a Staff Machine Learning Engineer to develop the content and creator classification systems that power discovery, recommendations, and platform insights.

This role is based in San Francisco or in New York and open to those who are able to be in-office 2 days per week on a hybrid work model.

About the Team

You'll join the Relevance team, whose mission is to use data to uncover key insights that drive Patreon's product strategy while instilling a culture of curiosity, scientific rigor, and data fluency across the organization. As the first dedicated Taxonomy MLE, you'll sit within the Relevance team — responsible for powering discovery, feed relevance, and creator-fan matching. You'll collaborate closely with the team on shared infrastructure, code reviews, and roadmap alignment, while partnering with Product, GTM, Trust & Safety, and other MLEs to ensure your classification systems serve the full breadth of Patreon's needs.

About the Role

  • Build and deploy machine learning pipelines that generate taxonomies of creators, content, and risk profiles across the platform. These systems enable everything from improved recommendations to content quality signals and search relevance.
  • Conduct exploratory data analyses and proof-of-concept machine learning models to understand opportunities and potential project impact.
  • Collaborate with cross-functional partners, such as product, engineering, design, legal, and trust and safety to design effective machine learning solutions.
  • Analyze and prepare training data, including using crowdsourcing data labeling techniques.
  • Train and iterate on machine learning models using novel techniques.
  • Deploy machine learning models to production and write backend code when necessary to properly deploy the model.
  • Debug models when observability shows performance gaps, and iterate on models.
  • Translate data into actionable insights and communicate your technical work to cross-functional partners, executive leadership, and the rest of the company.
  • Be a cultural and technical leader on the Relevance team.

About You

  • 8+ years of experience in ML engineering or applied ML roles
  • You have expertise in natural language processing, topic modeling, and clustering techniques
  • You have experience working in an end-to-end machine learning team environment: analyzing data, building and iterating on machine learning models, writing production-level code and shipping to production, monitoring performance, and A/B testing
  • You have experience working with unstructured content (e.g. audio, video, text, images) and understand how to extract meaning from complex media
  • You write clean and robust code in Python and have fluency in common ML/NLP libraries
  • You have experience with distributed systems, production pipelines, and model deployment frameworks
  • You collaborate effectively across engineering, data science, and product teams
  • You have solid communication skills and write clear documentation
  • Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related field

About Patreon

Patreon powers creators to do what they love and get paid by the people who love what they do. Our team is passionate about making this mission and our core values come to life every day in our work. Through this work, our Patronauts:

  • Put Creators First | They’re the reason we’re here. When creators win, we win.
  • Build with Craft | We sign our name to every deliverable, just like the creators we serve.
  • Make it Happen | We don’t quit. We learn and deliver.
  • Win Together | We grow as individuals. We win as a team.

We hire talented and passionate people from different backgrounds because workplace diversity and inclusion is critical to our ability to serve creators worldwide. If you’re excited about a role but your past experience doesn’t match with every bullet point outlined above, we strongly encourage you to apply anyway. If you’re a creator at heart, are energized by our mission, and share our company values, we’d love to hear from you.

Patreon is proud to be an equal opportunity employer. We provide employment opportunities without regard to age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class. If you need a reasonable accommodation during the interview process, please let us know via email at accomodations@patreon.

Patreon offers a competitive benefits package including and not limited to salary, equity plans, healthcare, flexible time off, company holidays and recharge days, commuter benefits, lifestyle stipends, learning and development stipends, patronage, parental leave, and 401k plan with matching.

Patreon operates under a hybrid work model, where employees based in office locations are expected to come into the office two days per week, excluding sick time and paid leave. The goal of this policy is to be intentional about the in-person time we spend together to strengthen the feeling of community at Patreon. Candidates outside of our office hubs are not expected to meet the same requirements.

At Patreon, we believe in fair and transparent pay. In compliance with New York and California pay transparency laws, we are sharing the expected salary range for this role.

The posted salary range is dependent on the location and the level. This range may encompass multiple levels within the role’s job family. The final offer will be based on candidate’s experience, skills, competencies, and geographic location, aligning with the appropriate job level within Patreon’s leveling framework. For remote employees located outside CA and NY, salary may vary based on location and local market conditions.

Patreon reserves the right to modify or update compensation and benefits at any time.

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Навыки

  • Data Analysis
  • A/B Testing
  • Python
  • Machine Learning
  • Distributed Systems
  • Natural Language Processing
  • Clustering
  • Topic Modeling

Возможные вопросы на собеседовании

Проверка экспертности в основной области задачи — классификации контента.

Как бы вы спроектировали иерархическую систему таксономии для платформы с таким разнородным контентом (текст, аудио, видео), как на Patreon?

Оценка навыков работы с данными и понимания качества обучения.

Какие стратегии краудсорсинга и активного обучения вы бы использовали для разметки данных, чтобы минимизировать шум в обучающей выборке?

Проверка опыта работы с современными методами обработки естественного языка.

Какие подходы к тематическому моделированию (topic modeling) наиболее эффективны для коротких описаний постов в сравнении с длинными текстами?

Оценка инженерных навыков и понимания жизненного цикла модели.

Опишите ваш опыт дебаггинга ML-моделей в продакшене: как вы отслеживаете деградацию качества и какие метрики мониторинга считаете критическими?

Проверка лидерских качеств и умения работать с бизнесом.

Как вы расставляете приоритеты между точностью модели и скоростью её вывода (latency) при согласовании требований с продуктовой командой?

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