Sr. Machine Learning Engineer, Siri Speech
Apple
Software Engineering, Data Science
Cupertino, CA, USA
USD 181,100-318,400 / year + Equity
Posted on May 8, 2026
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!
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!
- Design, train, and evaluate machine learning models for production use cases
- Build and maintain scalable ML pipelines (data ingestion, feature engineering, training, evaluation, serving)
- Collaborate with data scientists to translate research prototypes into robust, production-grade systems
- Monitor deployed models for performance degradation and data drift
- Optimize models for latency, throughput, and resource efficiency
- Contribute to ML infrastructure, tooling, and best practices
- 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
- 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