logo

Machine Learning @ Snap

Machine Learning-ingenieurs bij Snap werken end-to-end en beheersen het hele Machine Learning-systeem.

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.”

Teams in Machine Learning

Our machine learning engineers solve real world ML problems.

Monetisatie

Als Machine Learning Engineer in het Monetisatie-team bouw en optimaliseer je het hele advertentie-ecosysteem. Je zorgt voor een hoge relevantie en impact, niet alleen voor adverteerders en gebruikers maar voor heel Snap. Van hoogwaardige systemen voor realtime bieden of adserving en veilingen ontwerpen, laag- en hooggeplaatste advertenties personaliseren, oplossingen ontwikkelen voor advertentietargeting en -levering, je blijft ervoor zorgen dat advertenties naadloos in het platform worden geïntegreerd. Je traint modellen met miljarden voorbeelden, met behulp van multi-task learning, sequence modeling en modeling van gebruikersinteractie met advertenties. Onze modellen voorspellen demografische gegevens van gebruikers om de doelgroeptargeting te verbeteren met grafische neurale netwerken en content, zodat we kunnen begrijpen hoe ons werk de toekomst van het advertentieplatform van Snapchat vormgeeft.

Jouw taken:

  • AI-gedreven adverteren

  • Nieuwe gepersonaliseerde advertentieproducten en -ervaringen

  • Eigenaar van de belangrijkste inkomstenbron van Snap

  • Geavanceerde advertentieproducten ontwikkelen

Locations

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

ML @ Snap

Hoor van het team over het leven bij Snap in Machine Learning

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