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

Cupertino, California, United States -

Full Time

Start Date

Immediate

Expiry Date

08 May, 26

Salary

0.0

Posted On

07 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Deep Learning, Machine Learning, Reinforcement Learning, Python, JAX, PyTorch, Post-Training, Supervised Fine-Tuning, Data Quality Assessment, Human Data Labeling, Synthetic Data Generation, Evaluation Methodologies, Model Performance, Distributed Training, Collaboration, Communication

Industry

Computers and Electronics Manufacturing

Description

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 MINIMUM QUALIFICATIONS 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. PREFERRED QUALIFICATIONS 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;

Responsibilities

You will work with a team to tackle challenging problems in foundation model post-training. Your focus will be on defining training recipes to enhance base models into highly capable assistants.