Job Description
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.
We’re looking for a Machine Learning Engineer to join Snap Inc! Working from our Mountain View, CA office you will be tasked with solving interesting technical challenges such as architecting and deploying infrastructure to handle our scale, designing a slick and secure mobile client, and maintaining software used by millions every day.
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
Ability to prioritize tasks and work independently
Excellent verbal and written communication skills, with high attention to detail
Experience collaborating with internal and external stakeholders at all levels of a company
Experience in solving open ambiguous problems from end to end
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
5+ years ML industry experience or 4+ years ML industry experience and MS and/or PhD in computer science or related field
Preferred Qualifications:
Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks
Experience with mobile apps and/or databases
Experience working with distributed systems
M.S. degree and/or PhD in computer science or related field
Experience working with machine learning, ranking infrastructures, and system designs
Ability to proactively learn new concepts and apply them at work
