Senior Generative ML Engineer - On-Device AR

Snap Inc.

New York (NY)

On-site

USD 229,000 - 343,000

Full time

2 days ago
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Benefits offered by this job

Paid parental leave
Comprehensive medical coverage
Emotional and mental health support
RSU equity

Job summary

Snap Inc. is seeking a Machine Learning Engineer to develop innovative ML technology that powers experiences for millions of Snapchatters. You will build AR experiences using generative models and deliver on-device ML experiences, collaborating with cross-functional teams to prototype new products.

The role requires deep ML expertise, proficiency with state-of-the-art frameworks, and a passion for solving open, ambiguous problems while continuously growing professionally.

Qualifications

  • Minimum 8+ years of post-Bachelor’s machine learning or related experience; or a Master’s degree + 7+ years; or a PhD + 4+ years.
  • Experience with Computer Vision or Generative Modeling techniques.
  • Experience with ML frameworks such as TensorFlow, PyTorch, JAX, MLX, scikit-learn, or related frameworks.

Responsibilities

  • Develop innovative machine learning technology and products that serve millions of Snapchatters
  • Build cutting-edge augmented reality experiences using generative models
  • Deliver generative machine learning experiences on device
  • Partner with cross-functional Snap teams to explore and prototype new products

Skills

ML
Computer Vision
Generative Modeling
TensorFlow/PyTorch/JAX
Research/Prototyping

Education

PhD in related field
Masters in technical field
Bachelor's in CS/Math

Tools

TensorFlow
PyTorch
JAX
scikit-learn

Job description

Snap Inc. is seeking a Machine Learning Engineer to develop innovative ML technology that powers experiences for millions of Snapchatters. You will build AR experiences using generative models and deliver on-device ML experiences, collaborating with cross-functional teams to prototype new products.

The role requires deep ML expertise, proficiency with state-of-the-art frameworks, and a passion for solving open, ambiguous problems while continuously growing professionally.

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