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Machine Learning Engineer, 3+ Years Experience - San Francisco

AT Snap
Snap

Machine Learning Engineer, 3+ Years Experience - San Francisco

San Francisco, CA

Snap Inc. is a camera company. We believe that reinventing the camera represents our greatest opportunity to improve the way people live and communicate. Our products empower people to express themselves, live in the moment, learn about the world, and have fun together.

Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.

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Some core features we build and maintain include Snapchat’s Camera, Creative Tools, Maps, Chat, Memories, Stories, Discover, Games, and Minis. Our Infrastructure teams deliver an innovative and cost-efficient platform that ensures Snapchat is the fastest way to communicate with your friends, no matter where you are in the world. We have one of the fastest growing digital ad platforms, and our Monetization teams drive measurable returns for advertisers through novel ad formats like Augmented Reality. As a Snap Engineering team member, you’ll help us build the future of communication.

 

We’re looking for a Machine Learning Engineer to join Snap Inc! This role will be based from our San Francisco, CA headquarters.

What you’ll do:

  • Create models which help drive value for users, advertisers, and our company

  • Evaluate the technical tradeoffs of every decision

  • Perform code reviews and ensure exceptional code quality

  • Build robust, lasting, and scalable products Iterate quickly without compromising quality


Knowledge, Skills & Abilities:

  • Strong understanding of machine learning approaches and algorithms

  • Able to prioritize duties and work well on your own

  • Background working with both internal and external partners

  • Background in solving open ambiguous problems

  • Possesses a desire to learn and help others


Minimum Qualifications:

  • BS/BA degree in technical field such as computer science, mathematics, statistics or equivalent years of experience

  • 3+ years ML industry experience or or PhD in computer science, mathematics, statistics, physics or related field


Preferred Qualifications:

  • Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks

  • MS degree and/or PhD in computer science or related field 

  • Experience working with machine learning, ranking infrastructures, and system design

At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets. If you have a disability or special need that requires accommodation, please don’t be shy and contact us at accommodations-ext@snap.com.

Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid maternity & paternity leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success!

Job ID: 2a572d0f33ec4bf5889f726be870ee734864d7465de0da26aeb76f1e87041954
Employment Type: Other