Machine Learning Engineer, Large Visual Generative Model Optimization

4 Months ago • All levels • $143,100 PA - $264,200 PA
Research Development

Job Description

We are seeking a Machine Learning System Engineer with a strong background in ML optimization and deployment, specifically for visual content generation within Apple Intelligence. Responsibilities include designing, implementing, and deploying innovative conditional visual generative models. The role involves overseeing software workflows, test protocols for model assembly and deployment, and conducting R&D in efficient neural network development like quantization and pruning. This involves managing various stages of the large visual model lifecycle, including training, optimization, deployment, validation, and evaluation. Effective communication of results is crucial within a multi-functional team.
Must Have:
  • Designing, implementing, and deploying generative models
  • Overseeing software workflows and test protocols
  • R&D in efficient neural network development
  • Handle model lifecycle stages like training, optimization, and deployment

Add these skills to join the top 1% applicants for this job

model-deployment
algorithms
machine-learning

We are looking a Machine Learning System Engineers who has a proven background at the intersection of ML optimization and ML model deployment. This would be in service of visual content generation initiatives that form an integral part of Apple Intelligence. Your primary responsibilities will include: Designing, implementing, and deploying innovative conditional visual generative models Overseeing software workflows and test protocols for assembling and deploying visual generative models Performing R&D in emerging areas of efficient neural network development including quantization, pruning, compression algorithms with a focus on visual generation models. Handle various components in the model lifecycle of such large visual model, range from training, optimization, deployment, validation, and evaluation. Effectively communicating results and insights in a highly multi-functional team

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