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機器學習 @ Snap

Snap 的機器學習工程師進行端對端（end-to-end）工作，並全權負責各自的機器學習系統。

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

Dynamic Ads


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.”

機器學習中的團隊

Our machine learning engineers solve real world ML problems.

營利

作為營利團隊的機器學習工程師，您將打造並最佳化整個廣告生態系統。 您不僅會為廣告商和用戶，甚至將為整個 Snap 帶來高度相關性和高影響力。 從為即時競價或廣告服務與拍賣設計高效能的系統，到為輕型和重型排名工具個人化，乃至於為廣告定位與交付建立解決方案，您將繼續確保廣告在整個平台上無縫整合。 您會使用多工作業學習、序列建模，以及用戶與廣告互動建模，以數十億個範例訓練模型。 我們的模型預測用戶人口統計資料，藉助圖形神經網路和內容改善受眾目標，以了解我們的工作如何塑造 Snapchat 廣告平台的未來。

您要處理的工作包括：

  • AI 驅動的廣告

  • 全新個人化廣告產品和體驗

  • Snap 的主要收入來源的擁有者

  • 開發尖端廣告產品

Locations

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

ML @ Snap

聽取團隊分享 Snap 機器學習的生活

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