Staff Machine Learning Engineer, 8+ Years of Experience

Posted 5 Days Ago
Be an Early Applicant
3 Locations
189K-333K Annually
Senior level
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
As a Staff Machine Learning Engineer at Snap Inc., you will create models that drive user value, evaluate technical decisions, perform code reviews, build scalable products, and work collaboratively with partners while navigating complex problems.
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.

​We’re looking for a Staff Machine Learning Engineer to join Snap Inc!

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

  • Ability to work with both internal and external partners

  • Skilled at solving open ambiguous problems

  • Strong collaboration and mentorship skills

Minimum Qualifications:

  • Bachelor's degree in technical field such as computer science, mathematics, statistics or equivalent years of experience

  • 8+ years machine learning experience in industry

Preferred Qualifications:

  • Advanced degree in computer science or related field

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

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

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 $222,000-$333,000 annually.


 

Zone B:

The base salary range for this position is $211,000-$316,000 annually.

Zone C:

The base salary range for this position is $189,000-$283,000 annually.

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

Top Skills

Machine Learning

What the Team is Saying

Xiaolin
Yvette
Matt
Jasmeet
Xueyin (Sherry)
Amir
Jung
Xu
The Company
HQ: Santa Monica, CA
5,000 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
Machine Learning @ Snap
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
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