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Machine Learning @ Snap

Ang mga Machine Learning Engineers sa Snap ay nagtatrabaho end-to-end at nagmamay-ari ng buong Machine Learning system.

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.”

Mga Team sa Machine Learning

Our machine learning engineers solve real world ML problems.

Monetization

Bilang Machine Learning Engineer sa Monetization team ikaw ay gagawa at magpapaganda ng buong ad ecosystem. Ikaw ay maghahatid ng high-relevance at high-impact hindi lang para sa mga advertiser at mga user ngunit para sa lahat sa Snap. Mula sa pagdidisenyo ng mga high-performance system hanggang sa real-time bidding o ad serving at mga auction, pagpe-personalize ng mga light at heavy ranker, paglikha ng mga solusyon para sa ad targeting at delivery, magpapatuloy ka sa paninigurado sa maayos na integration ng mga ad sa buong platform. Tuturuan mo ang mga model sa mga bilyon na mga halimbawa, gamit ang multi-task learning, sequence modeling at user x ad interaction modeling. Hinuhulaan ng aming mga model ang mga user demographic para mapaganda ang audience targeting gamit ang mga graph neural network at content, para maintindihan kung paano hinuhubog ng aming trabaho ang kinabukasan ng ad platform ng Snapchat.

Ano ang mga gagawin mo:

  • Advertising na AI-driven

  • Bagong personalized na mga produkto ng ad at mga karanasan

  • May-ari ng main revenue driver ng Snap

  • Pagbuo ng mga makabagong produkto ng ad

Locations

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

ML @ Snap

Makinig mula sa team tungkol sa Buhay sa Snap sa 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