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
Job Summary
Develop innovative machine learning technology and products that serve millions of Snapchatters, specifically 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. Focus on developing the full stack of generative AI, including foundational models, efficient infrastructure, and on-device and server-side inference for multimodal LLMs, video generation, and real-time AR. This role requires 8+ years of post-Bachelor's machine learning experience and familiarity with Neural Networks, Deep Learning, and Generative Modeling. The position is based in the US with a "default together" policy requiring 4+ days in the office. Base salary ranges from $195,000 to $343,000 annually depending on location, with eligibility for equity in the form of RSUs.
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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