logo

Maskinlæring @ Snap

Hos Snap arbeider maskinlæringsteknikere med hele prosessen og har eierskap over hele maskinlæringssystemet.

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.”

Teamene innen maskinlæring

Our machine learning engineers solve real world ML problems.

Inntektsgenerering

Som maskinlæringsingeniør på inntektsgenereringsteamet vil du bygge og optimalisere hele annonseøkosystemet. Du vil skape høy relevans og stor påvirkning, ikke bare for annonsører og brukere, men for hele Snap. Fra å designe høyytelsessystemer for sanntidsbudgivning eller annonselevering og auksjoner, personalisere lette og tunge rangeringer, lage løsninger for annonsemålretting og levering, vil du fortsette å sikre sømløs integrasjon av annonser på tvers av plattformen. Du vil trene modeller på milliarder av eksempler, ved å bruke multi-task læring, sekvensmodellering og bruker x annonse interaksjonsmodellering. Våre modeller forutsier brukerens demografi for å forbedre målretting av publikum med graf-nevrale nettverk og innhold, for å forstå hvordan arbeidet vårt former fremtiden til Snapchats annonseplattform.

Hva du vil jobbe med:

  • AI-drevet annonsering

  • Nye personaliserte annonseprodukter og -opplevelser

  • Eier av Snap sin hovedinntektskilde

  • Utvikling av banebrytende annonseprodukter

Locations

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

ML @ Snap

Hør fra teamet om livet i Snap innen maskinlæring

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