AIML Machine Learning Researcher, Post-Training for Foundation Models

We are a group of engineers and researchers responsible for building foundation models at Apple. Within this group the Post-Training work streams focus on transforming powerful pre-trained checkpoints into helpful high-quality models that power billions of Apple products. We are looking for researchers who are passionate about foundation model post-training including Supervised Fine-Tuning (SFT) Reinforcement Learning with experiences in core capabilities such as instruction following tool use deep thinking and reasoning.

We build frontier foundation models that power intelligent experiences at Apple. Our team works across the full training lifecycle: including pre-training foundation models and developing mid-training approaches that bridge general capability and task-specific performance. What makes our work distinct is that were engineering models specifically for Apple silicon and optimized for experiences that are private personal and deeply integrated into the OS. Were solving frontier problems in reward modeling to resist reward hacking handling sparse and delayed rewards in agentic settings and aligning models reliably across the spectrum from open-ended creative tasks to precise action-taking workflows. If youre drawn to hard problems where the research and the product are inseparable this is the team.

Recipe Development: Design and iterate on end-to-end post-training recipes combining SFT Reinforcement Learning and reasoning regimes to achieve specific model behaviors and Research: Develop and implement novel algorithms for preference optimization model steering and Strategy: Research methods for high-quality human and synthetic data generation automated data filtering and curriculum learning to improve instruction following and reasoning : Design robust evaluation frameworks to measure model helpfulness factuality and utility moving beyond static benchmarks to capture real-world : Work closely with pre-training teams to inform architecture choices and with product teams to understand user requirements.

Demonstrated expertise in deep learning with a focus on LLMs post-training or reinforcement learning backed by a strong publication record or real world experiences and accomplishments in these or closely related programming skills in Python and one of the deep learning frameworks such as JAX or or equivalent practical experience in Computer Science Machine Learning or a related technical field.

Proven track record in post-training: Specialization in post-training algorithms techniques and best practices for large foundation models with proven track -training data: Deep experiences with human data labeling synthetic data generation and data quality assessment for foundation models; Evaluation methodologies: Deep experience in evaluating data and training recipe and deeply understand the model building iterative process and life Research: Experience in improving model performance on reasoning tasks (math coding logic).nScale u0026 Systems: Experience training SOTA large models at scale and familiarity with distributed training challenges and understand the communication and collaborative skills: Strong communication skills and a passion for collaboration within and across teams.