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機械学習 @ Snap

Snapの機械学習エンジニアはエンドツーエンドで作業し、それぞれの機械学習システム全体を担当しています。

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.”

機械学習チーム

Our machine learning engineers solve real world ML problems.

収益化

収益化チームの機械学習エンジニアとして、広告エコシステム全体を構築し、最適化します。広告主やユーザーだけでなく、Snap全体にとっても、関連性とインパクトが高いことを推進します。リアルタイム入札や広告配信、オークション用の高性能システムの設計、上位、下位のランクの広告のパーソナライズ、広告のターゲティングと配信のためのソリューションの作成から、プラットフォーム全体で広告がシームレスに統合されるよう努めます。マルチタスク学習、シーケンスモデリング、ユーザー×広告インタラクションモデリングを使用することにより、数十億の例に基づいてモデルをトレーニングします。私たちのモデルは、ユーザーの人口統計を予測し、グラフ的なニューラルネットワークとコンテンツでオーディエンスのターゲティングを改善し、私たちの取り組みがどのようにSnapchatの広告プラットフォームの未来を形作るかを理解します。

取り組むこと：

  • AIが主導する広告

  • パーソナライズされた新しい広告製品と体験

  • Snapの主な収益の推進要因の所有者

  • 最先端の広告製品の開発

Locations

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

ML @ Snap

機械学習チームに聞く、Snapでのやりがいについて

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