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

Apple· Machine Learning and AI
Apply on company site
Cupertino, CA$184.7k - $324.8kfull-timePosted 6mo ago

About the role

Researcher focused on post-training of foundation models, including supervised fine-tuning, reinforcement learning, and reasoning, tailored for Apple silicon and private, OS-integrated experiences. Works across the full training lifecycle and collaborates with pre-training and product teams.

Responsibilities

  • 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 capabilities.
  • Algorithm Research: Develop and implement novel algorithms for preference optimization, model steering, and safety.
  • Data Strategy: Research methods for high-quality human and synthetic data generation, automated data filtering, and curriculum learning to improve instruction following and reasoning capabilities.
  • Evaluation: Design robust evaluation frameworks to measure model helpfulness, factuality, and utility, moving beyond static benchmarks to capture real-world performance.
  • Collaboration: Work closely with pre-training teams to inform architecture choices and with product teams to understand user requirements.

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.
  • 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.

Skills

Required:Deep learning with LLMsPythonJAX or PyTorch

Benefits

  • Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, reimbursement for certain educational expenses — including tuition.
  • Discretionary employee stock programs and eligibility for restricted stock units and Employee Stock Purchase Plan.
  • Potential for discretionary bonuses or commission payments as well as relocation.
Last updated Jul 3, 2026. Interested? Apply on the company site.

About Apple

Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment, and expert device support.

Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment, and expert device support.

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