Principal Machine Learning Engineer, Ads Marketplace

Posted 8 Days Ago
Be an Early Applicant
2 Locations
208K-366K Annually
Expert/Leader
Artificial Intelligence • Cloud • Machine Learning • Mobile • Software • Virtual Reality • App development
We believe the camera presents the greatest opportunity to improve the way people live and communicate.
The Role
The Principal Machine Learning Engineer will drive the technical roadmap for Snap's Ad Marketplace, designing and optimizing machine learning models and systems for efficient ad delivery. Responsibilities include collaborating with teams on machine learning strategies, staying current with technology trends, and mentoring within the ML community.
Summary Generated by Built In

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’s three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles.

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.

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 Principal Machine Learning Engineer to join Snap!

What you’ll do:

  • Drive the technical roadmap of our Ad Marketplace and Pacing teams, helping advance the sophistication of our systems, optimize ad spend and delivery, and make our auction more efficient

  • Design, implement, and scale machine learning models and control systems to efficiently and optimally run our ad marketplace

  • 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

  • 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:

  • Machine learning, including high scale and low latency models

  • Strong understanding of practical aspects of delivery and optimization systems, including control systems or reinforcement learning, as well as exploration systems

  • 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 experience with machine learning, control systems, or reinforcement learning

  • Experience in online advertising, including ad auction and marketplace optimization

  • Experience developing and shipping performant and scalable machine learning models

  • Experience with TensorFlow, PyTorch, or related deep learning frameworks

Preferred Qualifications:

  • 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

If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information.

"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 through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. 

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.

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 parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success!

Compensation

In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.

Zone A (CA, WA, NYC):

The base salary range for this position is $244,000-$366,000 annually.


 

Zone B:

The base salary range for this position is $232,000-$348,000 annually.

Zone C:

The base salary range for this position is $208,000-$311,000 annually.

This position is eligible for equity in the form of RSUs.

Top Skills

Machine Learning

What the Team is Saying

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Xueyin (Sherry)
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The Company
San Francisco, CA
5,500 Employees
Hybrid Workplace
Year Founded: 2011

What We Do

Snap Inc. is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. We contribute to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.

Why Work With Us

Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.

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Snap Inc. Teams

Team
Product + Tech Team
About our Teams

Snap Inc. Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

Our “default together” approach is an 80/20 model where we are asking team members to spend 80% of the time, on average, in the office, with the remaining 20% of the time spent remote.

Typical time on-site: 4 days a week
San Francisco, CA

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