Machine Learning Engineer

Design machine learning models to run on consumer devices for understanding speech and text as part of an interactive real-time dialogue system. Utilize data processing frameworks like Spark to pre-process datasets for training and evaluating ML models. Build end-to-end pipelines for automating data and modeling workflows, to achieve rapid prototyping and development of new models.

• Design machine learning models to run on consumer devices for understanding speech and text as part of an interactive real-time dialogue system
• Utilize data processing frameworks like Spark to pre-process datasets for training and evaluating ML models
• Build end-to-end pipelines for automating data and modeling workflows
• Adopt the latest Cloud computing infrastructure into data and modeling pipelines
• Adopt the latest neural architectures, such as DNN, RNN, LSTM, Transformer
• Leverage speech recognition systems, text-based natural language models, and LLMs to generate data and signals for detecting a user's intent to interact with a device
• Analyze production datasets to obtain actionable insights on how to improve model accuracy
• Combine all of these skills to deliver ML models for real-world features such as Hey Siri, Just Siri, Back-to-Back Requests, and Apple Intelligence

• Master's degree or foreign equivalent in Computer Science, Electrical Engineering, Machine Learning, Computer Engineering, or related field
• 3 years of work experience in the job offered or related occupation
• Training speech-based and text-based ML models for interactive dialogue systems
• Utilizing ML neural architectures including DNN, RNN, LSTM, or Transformer
• Analyzing datasets and design ML training recipes to improve accuracy
• Designing pipelines to synthesize ML training data with Large Language Model prompts
• Designing and implementing end-to-end data and machine learning pipelines for training and deployment of ML models
• Utilizing Python modules for machine learning
• Utilizing Spark for large-scale data processing
• Implementing automated deployment pipelines using Jenkins for CI/CD
• Utilizing GitHub for source code management
• Creating and presenting technical documentation

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses
• Discretionary restricted stock unit awards
• Employee Stock Purchase Plan
• Discretionary bonuses or commission payments
• Relocation assistance

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