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Pembelajaran Mesin @ Snap

Insinyur Pembelajaran Mesin di Snap bekerja secara end-to-end dan memiliki seluruh sistem Pembelajaran Mesin masing-masing.

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.”

Tim dalam Pembelajaran Mesin

Our machine learning engineers solve real world ML problems.

Monetisasi

Sebagai Machine Learning Engineer di tim Monetisasi, Anda akan membangun dan mengoptimalkan seluruh ekosistem iklan. Anda akan mendorong relevansi tinggi dan dampak tinggi tidak hanya bagi pengiklan dan pengguna, tetapi juga untuk semua Snap. Mulai merancang sistem berkinerja tinggi untuk penawaran dan lelang secara real-time atau penayangan iklan, mempersonalisasi peringkat ringan dan berat, membuat solusi untuk penargetan dan pengiriman iklan, Anda akan terus memastikan integrasi iklan yang lancar di seluruh platform. Anda akan melatih model berdasarkan miliaran contoh, menggunakan pembelajaran multi-tugas, pemodelan urutan, dan pemodelan interaksi pengguna x iklan. Model kami memprediksi demografi pengguna untuk meningkatkan penargetan pemirsa dengan jaringan neural grafis dan konten, untuk memahami bagaimana pekerjaan kita membentuk masa depan platform iklan Snapchat.

Apa yang akan Anda kerjakan:

  • Iklan berbasis AI

  • Produk dan pengalaman iklan yang dipersonalisasi baru

  • Pemilik pendorong utama pendapatan Snap

  • Mengembangkan produk iklan mutakhir

Locations

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

ML @ Snap

Dengarkan dari tim tentang Kehidupan di Snap dalam Pembelajaran Mesin

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