Join the Siri Planner team as an experienced Machine Learning Engineer and play a pivotal role in shaping the future of virtual assistants. You’ll contribute to revolutionizing how millions of Siri users worldwide interact with their Apple devices by building ground breaking AI solutions.

DESCRIPTION

As a Senior Machine Learning Engineer, you will lead initiatives to significantly advance Siri’s natural language understanding and planning capabilities using innovative LLM technologies. YOUR CORE RESPONSIBILITIES WILL INCLUDE: • 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.

MINIMUM QUALIFICATIONS

• Advanced degree (MSc/PhD) in Machine Learning, Computer Science, or a related quantitative field; or BSc with 5+ years of relevant industry experience.
• 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).
• Excellent problem-solving, critical thinking, and interpersonal skills, with a collaborative attitude.

PREFERRED QUALIFICATIONS

• 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.

Please refer the Job description for details