Applied LLM Research Engineer, Input Experience

What you'll do

• Develop and refine training and evaluation pipelines for Apple Foundation Models.
• Implement emerging techniques in SFT, RLHF, data synthesis, and parameter-efficient fine-tuning.
• Contribute to all phases of model development from problem formulation to continuous improvement.
• Collaborate on defining new features that enhance the capabilities of Apple Intelligence.
• Work closely with cross-functional teams to integrate models into real-world products.

What we're looking for

• PhD in CS/EE/Physics/Statistics or equivalent experience with 2+ years of relevant work.
• Strong expertise in ML and LLM principles, techniques, and practical applications.
• Experience with post-training techniques like SFT, RLHF, data synthesis, and PEFT.
• Proficiency in training frameworks such as PyTorch, JAX, TensorFlow, or equivalents.
• Familiarity with fine-tuning large models for real-world products and synthesizing datasets.
• Knowledge of advanced reasoning techniques and reward modeling for LLMs.