Sr. Machine Learning Engineer, Siri Speech

Apple Apple · Big Tech · Cupertino, CA · Machine Learning and AI

This role focuses on advancing Siri's conversational AI capabilities by designing, training, and evaluating machine learning models for production use cases. It involves building and maintaining scalable ML pipelines, optimizing models for performance, and contributing to ML infrastructure. The role requires experience across the full ML lifecycle, from data processing to deployment, with a focus on speech synthesis and recognition, natural language understanding, and dialog generation.

What you'd actually do

  1. Design, train, and evaluate machine learning models for production use cases
  2. Build and maintain scalable ML pipelines (data ingestion, feature engineering, training, evaluation, serving)
  3. Collaborate with data scientists to translate research prototypes into robust, production-grade systems
  4. Monitor deployed models for performance degradation and data drift
  5. Optimize models for latency, throughput, and resource efficiency

Skills

Required

  • Python
  • ML frameworks (PyTorch, TensorFlow, JAX)
  • Full ML lifecycle experience
  • Distributed training
  • Large-scale data pipelines
  • ML fundamentals
  • Cloud platforms (AWS, GCP, or Azure)
  • Containerization (Docker, Kubernetes)
  • Software engineering practices (testing, code review, version control)

Nice to have

  • PhD in Machine Learning, Computer Science, or a related field
  • LLMs
  • pre-training
  • fine-tuning
  • RL
  • MLOps tools (MLflow, Weights & Biases, Kubeflow)
  • Audio generation
  • Speech-to-speech
  • NLP
  • Real-time serving infrastructure

What the JD emphasized

  • production use cases
  • scalable ML pipelines
  • production-grade systems
  • deployed models
  • resource efficiency
  • MSc in Computer Science, Machine Learning, Statistics, or a related field
  • Proven experience in machine learning or a related engineering role
  • Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX)
  • Experience with the full ML lifecycle: data processing, training, evaluation, deployment
  • Familiarity with distributed training and large-scale data pipelines
  • Solid understanding of ML fundamentals: supervised/unsupervised learning, model evaluation, regularization
  • Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
  • Strong software engineering practices: testing, code review, version control
  • Experience with LLMs, pre-training, fine-tuning, RL
  • Background in a specific domain (audio generation, speech-to-speech, NLP)
  • Experience with real-time serving infrastructure

Other signals

  • Siri Conversational AI
  • foundation models
  • conversational AI
  • millions of Apple users