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机器学习 @ Snap

Snap 的机器学习工程师端到端工作，并拥有整个机器学习系统。

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.”

机器学习团队

Our machine learning engineers solve real world ML problems.

创收

作为创收团队的机器学习工程师，你将构建和优化整个广告生态系统。 你将不仅为广告商和用户，而且为整个 Snap 带来高相关性和高影响力。从为实时竞价或广告投放和竞价设计高性能系统，到对轻量级和重量级排名器进行个性化定制，再到为广告定向投放制定解决方案，你将持续确保广告在整个平台上无缝集成。 你将使用多任务学习、序列建模和用户与广告互动建模，在数十亿个示例上训练模型。 我们的模型通过图形神经网络和内容来预测用户的受众特征，以优化受众定位，从而了解我们的工作如何塑造 Snapchat 广告平台的未来。

你将致力于以下工作：

  • AI 驱动的广告

  • 全新的个性化广告产品和体验

  • Snap 主要收入驱动力的所有者

  • 开发前沿广告产品

Locations

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

ML @ Snap

听取团队关于 Snap 在机器学习中的生活

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