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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 we're engineering models specifically for Apple silicon and optimized for experiences that are private, personal, and deeply integrated into the OS. We're 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 you're drawn to hard problems where the research and the product are inseparable, this is the team.
Description
We believe that the most interesting problems in deep learning research arise when we try to apply learning to real-world use cases, and this is also where the most important breakthroughs come from. You will work with a close-knit and fast growing team of world-class engineers and scientists to tackle some of the most challenging problems in foundation models and deep learning.
Further, you will have opportunities to identify and develop novel applications of deep learning in Apple products. You will see your ideas improve the experience of billions of users.","responsibilities":"In this role, you will focus on pretraining, large language model (LLM) architecture, and scientific scaling of LLM. Experiences on full-stack LLM optimization such as mid-training, reinforcement learning, data research and kernel optimization (e.g. pallas and triton) will be a plus.
Preferred Qualifications
Reinforcement learning, on-policy distillation.
Post-training, mid-training large language models.
LLM context lengthening.
Minimum Qualifications
Demonstrated expertise in deep learning with publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, COLM, ACL, NAACL, EMNLP, ACL) or a track record in applying deep learning techniques to products
Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow
Ability to work in a collaborative environment.
Code large language models.
PhD, or equivalent practical experience, in Computer Science, or related technical field.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.