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SnapchatPosted 1 week ago

Staff Machine Learning Engineer, Generative AI Modeling and Inference

$229,000–$343,000 year

On-siteSeattle, Washington, United States or Los Angeles, California, United States

Full TimeSenior LevelDoctorate Or Professional DegreeLarge

Job Summary

Develop innovative machine learning technology and products that serve millions of Snapchatters, focusing on building cutting-edge augmented reality experiences using generative models. Deliver generative machine learning experiences on device by partnering with cross-functional teams to explore and prototype new products. This role requires 8+ years of post-Bachelor's machine learning experience and proficiency with frameworks like TensorFlow, Pytorch, and JAX. The position is based in Snap's office with a "default together" policy requiring 4+ days per week in-person attendance. Candidates will work on foundational models, efficient infrastructure, and on-device inference for Snapchat, Specs, and other digital services.

Required Qualifications

  • Bachelor's degree in technical field such as computer science, mathematics, statistics or equivalent years of experience
  • 8+ years of post-Bachelor's machine learning or related experience
  • Master's degree in a technical field + 7+ years of post-grad ML or related experience
  • PhD in a related technical field + 4+ years of post-grad ML or related experience
  • Experience with Computer Vision or Generative Modeling techniques
  • Experience working with machine learning frameworks such as TensorFlow, PyTorch, JAX, MLX, scikit-learn, or related frameworks

Desired Qualifications

  • Advanced degree in computer science or related field
  • Experience training large-scale diffusion models for images, videos or 3D
  • Knowledge of distillation, quantization and model compression techniques
  • Knowledge of GPU, CPU, or NPU optimization techniques
  • Experience building and optimizing ML inference pipelines
  • Experience working with machine learning frameworks such as TensorFlow, PyTorch, JAX, MLX, scikit-learn, or related frameworks

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