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Senior Machine Learning Engineer, Recommendations
Отличная вакансия в известном бренде с сильной инженерной культурой. Предлагаются щедрые бенефиты (35 дней отпуска, релокация, опционы) и работа над продуктом мирового уровня с огромным объемом данных.
Сложность вакансии
Высокая сложность обусловлена требованиями к глубоким знаниям архитектур рекомендательных систем (two-tower, multi-objective ranking) и опытом работы с высоконагруженными ML-системами в продакшене. Дополнительно ожидается владение современными инструментами AI-assisted разработки.
Анализ зарплаты
Зарплата для Senior ML ролей в Берлине и Лондоне обычно находится в диапазоне 85,000–110,000 евро/фунтов. SoundCloud — это Tier-1 компания, поэтому их предложение, скорее всего, находится на верхней границе рынка или выше, учитывая пакет акций.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Machine Learning Engineer position within the Reco-Core team at SoundCloud. With over 400 million tracks, SoundCloud presents one of the most exciting challenges in the recommendation space, and I am eager to apply my expertise in building scalable ML systems to help listeners discover their next favorite artist.
In my previous roles, I have successfully shipped large-scale ML models to production, focusing on candidate generation and multi-objective ranking. I have extensive experience with Python, Spark, and two-tower architectures, which aligns perfectly with your technical stack. Furthermore, I am a strong advocate for MLOps best practices and pragmatic engineering, including the use of AI-assisted workflows like Cursor to accelerate the development lifecycle.
I am particularly drawn to SoundCloud's artist-first mission and your commitment to a flexible, inclusive work culture. I am confident that my background in recommendation systems and my passion for music will allow me to make a significant contribution to the Reco-Core team and the broader SoundCloud community.
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Присоединяйтесь к SoundCloud и создавайте алгоритмы рекомендаций, которые услышат миллионы!
Описание вакансии
SoundCloud empowers artists and fans to connect and share through music. Founded in 2007, SoundCloud is an artist-first platform empowering artists to build and grow their careers by providing them with the most progressive tools, services, and resources. With over 400+ million tracks from 40 million artists, the future of music is SoundCloud.
We are looking for a Senior Machine Learning Engineer to join our Reco-Core team. As a key Machine Learning Engineer on this team, you will have a unique opportunity to shape and lead the ML practice for one of SoundCloud's most critical product areas. You will partner closely with ML, data, and backend engineers to design, build, and deploy the models that help millions of listeners discover their next favorite track within our vast and unique catalog of music and audio.
Key Responsibilities:
- Design, build, and deploy end-to-end machine learning solutions for recommendations, focusing on candidate generation, personalization, user and items modeling, and core ranking
- Take ownership of the full ML lifecycle, from prototyping and evaluation to deploying scalable models into production and monitoring their performance
- Strengthen and scale the foundation for ML engineering and MLOps within the Reco-Core team, defining best practices and building tooling to enable rapid iteration
- Partner with analysts and data scientists to translate research into production-ready systems
- Collaborate with other ML engineers to leverage shared infrastructure and knowledge
- Leverage agentic workflows and AI-assisted engineering as a force multiplier to work at 10x the speed of traditional methods
Experience and Background:
- Strong industry experience in a quantitative field (e.g., Computer Science, Statistics, Machine Learning) and/or a Ph.D./M.Sc. in a related discipline
- 3+ years of professional experience shipping large-scale ML models to production
- Strong engineering background in Python or Scala, with hands-on experience using data processing frameworks (e.g., Spark, Confluent) and vector search or nearest neighbor technologies
- Deep understanding of core recommendation concepts, including collaborative filtering, matrix factorization, deep learning for recommendations (e.g., two-tower architectures), and multi-objective ranking, with the ability to discuss trade-offs between different approaches
- Proven experience building and scaling ML infrastructure and tooling for model deployment, lifecycle management, and monitoring in a cloud environment (GCP, AWS, or Azure)
- Pragmatic approach to engineering efficiency, with a habit of leveraging modern AI-assisted workflows (e.g., Cursor, Claude Code) to accelerate development and research
About us:
- We are a multinational company with offices in the US (New York and Los Angeles), Germany (Berlin), and the UK (London)
- We provide a flexible work culture that offers the opportunity to collaborate and connect in person at our offices as well as accommodating work from home
- We are deeply committed to ensuring diversity, equity and inclusion at all levels of our organization and fostering a community where everyone’s voice, perspective and experience is respected and heard.
- We believe a strong team is made by investing in employees through mentorship, workshops and enrichment opportunities
Benefits:
- Not located in Berlin? No worries, we offer extensive relocation support including allowances, one way flights, temporary accommodation and, by partnering with Expath, on the ground support on arrival
- Interested in a gym membership, photography course or book? We have a Creativity and Wellness benefit!
- Employee Equity Plan
- Generous professional development allowance
- Flexible vacation and public holiday policy where you can take up to 35 days of PTO annually
- We offer free German courses at beginning, intermediate and advanced
- Various snacks, goodies, and 2 free lunches weekly when at the office
Diversity, Equity and Inclusion at SoundCloud
SoundCloud is for everyone. Diversity and open expression are fundamental to our organization; they help us lead what’s next in music by understanding and empowering our creators and fans, no matter their identity. We acknowledge the challenges in the music industry, and strive to influence an inclusive culture where everyone can contribute respectfully and thrive, especially the historically marginalized communities that many of our creators, fans and SoundClouders identify with. We are dedicated to creating an inclusive environment at SoundCloud for everyone, regardless of gender identity, sexual orientation, race, ethnicity, migration background, national origin, age, disability status, or care-giver status.
At SoundCloud you can find your community or elevate your allyship by joining a Diversity Resource Group. Diversity Resource Groups are employee-organized groups focused on supporting and promoting the interests of a particular underrepresented community in order to build a more inclusive culture at SoundCloud. Anyone can join, whether you share the identity or strive to be an ally.
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Навыки
- AWS
- Azure
- Python
- GCP
- Machine Learning
- Deep Learning
- MLOps
- Recommendation Systems
- Spark
- Scala
- Vector Search
- Confluent
- Collaborative Filtering
Возможные вопросы на собеседовании
Проверка понимания специфики рекомендательных систем и умения работать с огромным каталогом SoundCloud.
Как бы вы спроектировали систему генерации кандидатов для каталога из 400 миллионов треков, чтобы обеспечить низкую задержку и высокую релевантность?
SoundCloud упоминает multi-objective ranking; важно понять, как кандидат балансирует разные метрики.
Как вы подходите к оптимизации нескольких целей одновременно (например, время прослушивания, лайки и подписки) в ранжировании?
Вакансия делает упор на MLOps и жизненный цикл моделей.
Опишите ваш опыт построения CI/CD пайплайнов для ML-моделей. Как вы обеспечиваете воспроизводимость экспериментов и мониторинг качества в продакшене?
В описании указано использование векторного поиска.
Какие технологии векторного поиска вы использовали и как вы решаете проблему обновления индексов при добавлении нового контента в реальном времени?
Проверка прагматичного подхода к разработке, упомянутого в вакансии.
Как вы используете AI-инструменты (например, Claude Code или Cursor) в своей повседневной работе для ускорения разработки без потери качества кода?
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