- Apple
- Cupertino, CA
- Full-Time
- 2 weeks ago
Sr. Machine Learning Engineer, Siri Speech: our view in 3 lines...
- The Role: Build and advance Siri conversational AI models, datasets, and infrastructure for speech and multi-modal interaction within Apple's Siri team.
- The Person: Design, train, evaluate, and deploy large-scale speech and conversational ML models and develop the supporting datasets and infrastructure for Siri.
- Requirements: Strong proficiency in Python and ML frameworks PyTorch TensorFlow JAX, experience with distributed training large-scale data pipelines cloud platforms AWS GCP or Azure and containerization Docker Kubernetes.
Job Description
We are a group of engineers/researchers responsible for advancing Siri Conversational AI at Apple. Our mission is to build cutting-edge infrastructure, datasets, and models that empower Siri with capabilities across natural language understanding, dialog generation, speech synthesis and recognition, and multi-modal interaction. We apply these technologies to create engaging, intelligent, and personalized conversational experiences for millions of Apple users!
Description
We believe that the most impactful breakthroughs in deep learning emerge when we address real-world problems at scale while we preserve user privacy. Siri presents a unique and rich set of challenges—from robust understanding of diverse user intents to fluid, contextual, and trustworthy multi-turn dialog. Join us, and we will take on the challenges to push the frontiers of foundation models and conversational AI!
Minimum Qualifications
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
Preferred Qualifications
PhD in Machine Learning, Computer Science, or a related field
Experience with LLMs, pre-training, fine-tuning, RL
Familiarity with MLOps tools (MLflow, Weights & Biases, Kubeflow)
Background in a specific domain (audio generation, speech-to-speech, NLP)
Experience with real-time serving infrastructure
