AIML Researcher/Engineer - Foundation Model Post-Training

Apple Inc

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Work type
On-site
Location
Seattle, WA
Posted
today

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Similar $208k
$153k most similar roles pay here $256k

This listing doesn't post a salary. Most similar roles pay $169,875–$246,150.

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About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 351 open roles on FindRole.

Listed pay typically runs $171,600–$272,100 across 246 roles with salary data.

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TL;DR · AIML Researcher/Engineer - Foundation Model Post-Training

As an AIML Researcher/Engineer at Apple’s Foundation Model team, you will join a dynamic group dedicated to advancing large-scale foundation models. Your primary responsibility involves designing and iterating on post-training strategies, including reinforcement learning, to enhance model capabilities for real-world applications. You will develop innovative algorithms for preference optimization and safety, while also driving data strategy through high-quality synthetic data generation and curriculum learning techniques. Additionally, you will collaborate with pre-training teams to inform architectural decisions and work closely with product teams to align user needs with model functionalities. This role requires expertise in deep learning frameworks like JAX or PyTorch, proficiency in Python, and a strong background in machine learning, ideally at the Masters/PhD level. Experience with large-scale distributed training and complex reasoning tasks is highly valued.

What you'll do

  • Design and refine post-training strategies to enhance model capabilities in instruction following and tool use.
  • Develop novel algorithms for preference optimization, model safety, and steering techniques.
  • Create high-quality data generation methods and automated filtering processes to improve training datasets.
  • Establish robust evaluation frameworks to measure model performance beyond static benchmarks.
  • Collaborate with pre-training teams to guide architectural decisions and align user needs with model functionalities.

What we're looking for

  • Demonstrated expertise in deep learning, LLMs, post-training, or reinforcement learning.
  • Proficient programming skills in Python and major deep learning frameworks like JAX or PyTorch.
  • Masters/PhD or equivalent experience in Computer Science, Machine Learning, or related field.
  • Experience training state-of-the-art large models at scale with distributed training knowledge.
  • Expertise in improving model performance on complex reasoning tasks.

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