AIML - Machine Learning Researcher, Post-training for Foundation Models

Apple • Cupertino, CA

13d

About The Position

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.

DESCRIPTION

We believe that the most interesting problems in deep learning research arise
when we try to bridge the gap between raw model capability and user-centric
utility. This is where the most important breakthroughs in model adaptation and
steering come from. You will work with a close-knit and fast-growing team of
world-class engineers and researchers to tackle some of the most challenging
problems in foundation model post-training. Your work will focus on defining the
training recipes that turn a base model into a highly capable assistant. This
involves research into existing and novel training data mix, algorithms and
evaluation methodologies

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 domains
• Proficient programming skills in Python and one of the deep learning frameworks such as JAX or PyTorch
• PhD or equivalent practical experience, in Computer Science, Machine Learning, or a related technical field.

Nice To Haves

• Proven track record in post-training: Specialization in post-training algorithms, techniques, and best practices for large foundation models with proven track record
• Post-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 cycle
• Reasoning Research: Experience in improving model performance on reasoning tasks (math, coding, logic)
• Scale & Systems: Experience training SOTA large models at scale and familiarity with distributed training challenges, and understand the trade-offs
• Strong communication and collaborative skills: Strong communication skills and a passion for collaboration within and across teams

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree