AIML - ML Researcher, AFM

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 are building the next generation of models optimized for Agentic, Reasoning, and Coding capabilities. This means training models via RL to reason from first principles, building autonomous coding agents that operate in real repositories, and developing agentic systems that handle multi-step workflows with error recovery. You will work on problems like: RL with verifiable rewards for mathematical reasoning, multi-turn RL for coding agents evaluated on SWE-Bench and beyond, scaling laws for RL compute allocation, progressive alignment across capability stages, and training models to manage their own context in long-horizon tasks. This is applied research with direct product impact - your work will ship to millions of users.

Preferred Qualifications

Reinforcement learning for LLMs: RLHF, GRPO, PPO, RLVR, reward modeling, RL scaling laws

Code generation and coding agents: repository-level code understanding, agentic coding

Agentic systems: multi-turn RL, tool-use planning, long-horizon task execution, user simulation

Distillation and alignment: on-policy distillation, reward-tilted distillation, cross-stage distillation to combine independently optimized capabilities into a single model

Long context and efficiency: sparse attention, context compression, scaling to very long context windows

Minimum Qualifications

Demonstrated expertise in deep learning with publications at top ML or NLP conferences, or a track record of 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.

PhD, or equivalent practical experience, in Computer Science, or related technical field.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .

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 $181,100 and $318,400, 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.