Machine Learning Engineer, Generative ML , Level 5

Posted Yesterday
Be an Early Applicant
2 Locations
Hybrid
178K-313K Annually
Senior level
Artificial Intelligence • Cloud • Machine Learning • Mobile • Software • Virtual Reality • App development
Snap is a technology company.
The Role
Design and implement generative ML systems (image, video, audio, multimodal LLMs) and deliver on-device and server-side inference. Build GenAI pipelines and AR experiences, prototype with cross-functional teams, and optimize efficient models for real-time mobile and wearable applications.
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’s Generative ML Platform team builds cutting-edge AI technologies that power creative, scalable experiences for hundreds of millions of Snapchatters worldwide. From multimodal LLMs and video generation to real-time AR, human understanding, and 3D content creation, we develop the full stack of generative AI, including foundational models, efficient infrastructure, and on-device and server-side inference. Our team creates intuitive tools, platforms, and agentic systems that empower creators, developers, and internal teams to bring ideas to life, while advancing personalized, human-centric experiences across mobile, web, and wearable devices like Spectacles.

We're looking for a Machine Learning Engineer to join our Generative ML team!

What you’ll do:

  • Develop innovative machine learning technology and products that serve millions of Snapchatters

  • Work on state of the art GenAI pipelines for image, video, and audio generation

  • Deliver generative machine learning experiences on device

  • Build cutting-edge augmented reality experiences using generative and diffusion models

  • Partner with cross-functional Snap teams to explore and prototype new products

 

Knowledge, Skills & Abilities:

  • A proven passion for machine learning; you stay up-to-date with research and are excited about prototyping new ideas quickly 

  • Strong software development skills in Python or C++

  • Proficiency working with major deep learning frameworks: PyTorch or TensorFlow

  • Knowledge of mathematics and deep learning foundations 

  • Desire to solve open ambiguous problems

  • Desire to grow professionally, learn and help others

  • Ability to effectively collaborate with internal teams and external partners

  • Ability to work independently

 

Minimum Qualifications: 

  • Bachelor’s Degree in a technical field such as computer science, mathematics, statistics or equivalent years of experience

  • 5+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 1 years of post-grad machine learning experience

  • Research or engineering experience in one or more of the following: generative models, efficient models, segmentation, object detection, classification, tracking, or other related applications of machine learning 

 

Preferred Qualifications:

  • Master's degree or PhD in a related technical field 

  • Experience developing real-time software for mobile applications 

  • Knowledge of GenAI, especially image, video, and audio generation foundations

  • Knowledge of efficient model foundations

  • Track record of successful projects in GenAI field

  • Examples of your work such as open source projects, blog posts, Kaggle contests, top conference or journal publications, etc. 

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.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).

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 $209,000-$313,000 annually.


 

Zone B:

The base salary range for this position is $199,000-$297,000 annually.

Zone C:

The base salary range for this position is $178,000-$266,000 annually.

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

Skills Required

  • Bachelor's degree in computer science, mathematics, statistics or equivalent experience
  • 5+ years post-Bachelor's machine learning experience (or equivalent Masters/PhD experience per listing)
  • Research or engineering experience in generative models, efficient models, segmentation, object detection, classification, or tracking
  • Strong software development skills in Python or C++
  • Proficiency with deep learning frameworks: PyTorch or TensorFlow
  • Knowledge of mathematics and deep learning foundations
  • Ability to collaborate cross-functionally and work independently
  • Master's degree or PhD in a related technical field
  • Experience developing real-time software for mobile applications
  • Knowledge of GenAI for image, video, and audio generation and efficient model foundations
  • Track record of successful GenAI projects and examples of work (open source, publications, blog posts, competitions)

What the Team is Saying

Xiaolin
Yvette
Matt
Jasmeet
Xueyin (Sherry)
Amir
Jung
Xu
Talia Mason
Maureen Ufomadu
Vincent Pagnard-Jourdan
Pulkit Trivedi
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The Company
HQ: Santa Monica, CA
5,000 Employees
Year Founded: 2011

What We Do

We contribute 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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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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