Principal Machine Learning Engineer, Signals and Measurement at Snapchat in Washington, DC

Job description

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company operates Snapchat , a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc. , a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji , Saturn, and other digital services. 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. We’re looking for a Principal Machine Learning Engineer to join our Signals & Measurement team at Snap! What you’ll do: Design and develop machine learning systems across the third-party signal lifecycle, from acquisition and curation to matching, attribution, and causal measurement Design, implement, and scale critical machine learning models to support Snap's monetization strategies Collaborate with cross-functional teams to set and align on machine learning strategies to meet company objectives Stay up-to-date with the latest technology in machine learning and apply this knowledge to tackle complex problems in innovative ways Collaborate with leadership to up-level the ML tech stack and improve the performance of the organization Work across teams to understand product requirements, evaluate trade-offs, and deliver the solutions needed to build innovative products or services Advocate for and apply best practices when it comes to availability, scalability, operational excellence, and cost management Provide technical direction that influences the entire ML community Knowledge, Skills & Abilities: Strong understanding of machine learning and deep learning approaches and algorithms, and their applications to advertising measurement, recommendation, and/or search domain Experience setting the direction for a team whose primary output is online ranking / recommendation models Ability to design, train, and optimize advanced machine learning models Excellent programming and software design skills Ability to proactively learn new concepts and technology and apply them at work Skilled at solving ambiguous problems and leading and executing complex technical initiatives Strong collaboration and mentorship skills Minimum Qualifications: Bachelor's in a technical field such as computer science, mathematics, statistics or equivalent years of experience 11+ years of industry machine learning experience Experience developing and shipping performant and scalable machine learning models for recommendation or ranking use cases Experience with TensorFlow, PyTorch, or related deep learning frameworks Preferred Qualifications: Experience with signals and measurement stack, identity resolution, anomaly detection or causal measurement Familiar with attribution and advanced measurement methodologies Experience in online advertising, including ad targeting, ranking, auction, and/or marketplace optimization. Advanced degree in a related field such as machine learning, computer vision, or mathematics Experience partnering with cross-functional executives and management across a globally distributed organization and exercising sound judgment Track record of delivery in rapidly changing, highly collaborative, multi-site, multi-stakeholder environments Experience working with a diverse group of engineers Experience contributing to AI publications "Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better thro