Advance Siri’s natural language understanding and planning capabilities using innovative LLM technologies to revolutionize how millions of Siri users worldwide interact with their Apple devices.
Requirements
- Proven hands-on experience in machine learning engineering for large-scale models, with a strong focus on generative AI, LLMs, Retrieval Augmented Generation (RAG), or agentic systems.
- Strong Python proficiency, including development, debugging, and design, coupled with extensive experience using ML frameworks (e.g. PyTorch, Jax, HuggingFace).
- Applying LLMs for synthetic data generation (e.g. for knowledge distillation) or applying reinforcement learning for post-training or fine-tuning of LLMs.
- A successful track record of building and deploying end-to-end ML data pipelines (data preparation, storage, training, and inference) in cloud or on-premise environments.
- Experience with training, fine-tuning, and deploying LLMs in production environments.
- Proficiency in evaluating LLMs for specific product tasks and performance metrics.
Responsibilities
- Developing innovative systems for synthetic training data generation and implementing strategies for the continuous optimization of model performance.
- Designing and implementing agentic workflows and RAG systems to enhance Siri’s capabilities.
- Optimizing model performance for tool calling and reasoning tasks.
- Actively staying at the forefront of academic and industry research in LLMs, NLP, and agentic systems, and translating novel insights into practical solutions.
- Collaborating closely with a multidisciplinary team of researchers, software engineers, and product designers to seamlessly integrate AI innovations into the Siri user experience.
- Applying LLMs for synthetic data generation (e.g. for knowledge distillation) or applying reinforcement learning for post-training or fine-tuning of LLMs.
- Building and deploying end-to-end ML data pipelines (data preparation, storage, training, and inference) in cloud or on-premise environments.
Other
- Advanced degree (MSc/PhD) in Machine Learning, Computer Science, or a related quantitative field; or BSc with 5+ years of relevant industry experience.
- Excellent problem-solving, critical thinking, and interpersonal skills, with a collaborative attitude.
