Machine Learning Engineer, Siri Speech

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! 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!
We are looking for a skilled Machine Learning Engineer to design, build, and deploy machine learning systems that solve real-world problems at scale. You will work closely with data scientists, software engineers, and product teams to bring ML models from research into production.
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, fine-tuning, RLHF Familiarity with MLOps tools (MLflow, Weights & Biases, Kubeflow) Background in a specific domain (audio generation, speech-to-speech, NLP) Experience with feature stores or real-time serving infrastructure