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Snap에서의 머신러닝

Snap의 머신러닝 엔지니어들은 처음부터 끝까지 모든 단계를 담당하며 각자 맡은 해당 머신러닝 시스템 전체를 책임집니다.

Open Roles

Our engineers utilize state of the art models and continually push the boundaries of what’s possible in Snapchat's content, monetization, infrastructure, features, and more!

AI Lenses

Unified Recommendations

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Machine Learning Engineering at Snap

As a Machine Learning Engineer at Snap you’ll drive Snapchat’s dynamic experience through the full lifecycle of advanced state-of-the-art models -  from data preprocessing, feature engineering, and model training, to deployment and ongoing optimizations. You’ll leverage cutting-edge techniques, ranking algorithms for ad relevance, recommendation engines for personalized content and NLP for enhanced interactions - all while processing petabytes of data for over 850 million users. 

Through both classic and deep learning models, you’ll create precise, responsive experiences that empower users to express themselves, connect and discover the world in real-time.


“The real benefit that Snap has is the size of our scale, and the breadth of influence and impact that people will have. You can run fast, have broad influence and actually see your work hit production with the right experimentation tools and infrastructure to be productive.”

머신러닝 내의 여러 팀

Our machine learning engineers solve real world ML problems.

수익 창출

수익 창출 팀의 머신 러닝 엔지니어로서 전체 광고 생태계를 구축하고 최적화하게 됩니다. 광고주와 사용자뿐만 아니라 Snap 전체에 높은 관련성과 큰 영향을 미칠 수 있습니다. 실시간 입찰 또는 광고 게재 및 경매를 위한 고성능 시스템을 설계하고, 라이트 및 헤비 랭커를 개인화하고, 광고 타겟팅 및 전송을 위한 솔루션을 만드는 것부터 플랫폼 전체에서 광고를 원활하게 통합할 수 있도록 계속 노력하게 됩니다. 멀티태스킹 학습, 시퀀스 모델링 및 사용자와 광고 상호 작용 모델링을 사용하여 수십억 개의 예제를 통해 모델을 훈련하게 됩니다. 저희 모델은 그래프 신경망과 콘텐츠를 통해 사용자 인구 통계를 예측하여 방문자 타겟팅을 개선하고, Snapchat의 광고 플랫폼의 미래를 만들어가는 방식을 파악합니다.

여러분이 할 작업은 다음과 같습니다.

  • AI 기반 광고

  • 새로운 맞춤형 광고 제품 및 경험

  • Snap의 주요 수익원 책임자

  • 최첨단 광고 제품 개발

Locations

Our RTO (Return to Office) policy is 4 times per week hiring in these office locations

Snap에서의 ML

팀원들이 말하는 Snap 머신러닝에서 일하기

We're Hiring!

Our interview process covers engineering, foundational, and applied ML.

Coding

Expect to solve algorithmic problems that test your proficiency in data structures, algorithms, and problem-solving skills. Focus on your ability to write clean, efficient, and well-documented code.

ML Fundamentals

You’ll be assessed on ML theory and core machine learning models, concepts, techniques and applications. Be prepared to discuss supervised and unsupervised learning, recommendation systems, ranking, model evaluation metrics, and optimization techniques.

ML Applied Design

Evaluates your ability to design and apply machine learning solutions to real-world problems. You may be asked to walk through the end-to-end process of selecting models, feature engineering, and evaluating performance. At times this can test your ability to problem solve in an ambiguous environment.

ML System Design

The focus will be on designing scalable and robust ML systems that can handle large-scale data and production environments. Expect to discuss the infrastructure and trade offs in architecture, model deployment strategies and system monitoring.

Our Interview Process