SWE - Applied Machine Learning Engineer - DeveloperPublications...

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• Applied Machine Learning Engineer - Developer Publications
  London, England, United Kingdom | Machine Learning and AI Add to
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  Publications Description As an Applied ML Engineer in the Developer
  Publications Intelligence team, you will join a multi-disciplinary
  team of passionate engineers to design, fine-tune, and produce
  models used by existing and future tools developed by Apple for
  third-party developers. We seek exceptional candidates to help
  shape the future of Swift code generation and beyond. Your role is
  to ensure Apple and third-party developers using Xcode deliver
  outstanding software products to millions of users worldwide. Your
  responsibilities will include: 1. Fine-tuning models and making
  them available for evaluation and experimentation by engineers 2.
  Engaging in all aspects of model development, from ideation and
  training to deployment 3. Collaborating with data collection, model
  evaluation, and tool integration teams to develop and implement
  model solutions 4. Developing and maintaining frameworks and tools
  to facilitate the model fine-tuning process Minimum Qualifications
  
• Proficiency with open-source ML frameworks such as PyTorch,
  TensorFlow, or OpenNMT - Strong programming skills in Python,
  C/C++, Swift, or other languages Preferred Qualifications - BS, MS,
  or PhD in Computer Science, Artificial Intelligence, or Machine
  Learning (or equivalent experience) - Experience with Machine
  Learning, especially Large Language Models (LLMs) that generate
  code - Ability to interpret and apply cutting-edge research to
  customer-facing products and developer tools - Hands-on experience
  with fine-tuning, model recipes, and training models - Experience
  adapting re-trained LLMs for downstream tasks - Experience using
  Xcode for software development on Apple platforms - Proficiency in
  Swift programming language - Knowledge of Apple’s APIs such as
  SwiftUI and Foundation - Ability to work cross-functionally across
  disciplines #J-18808-Ljbffr