below marketmost similar roles pay hereabove market
This role pays more than
79%
of similar roles. Most pay
$202,800–$254,750
— the blue band above.
At the midpoint, this role pays about
$255k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
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
3497 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2702 roles with salary data.
ML Researcher, Apple Foundation Models joins the team building frontier foundation models optimized for Apple silicon and integrated OS experiences. This role involves working across the full training lifecycle, including pre-training and developing mid-training approaches to bridge general capabilities with task-specific performance. You will build models for agentic, reasoning, and coding capabilities by training via RL to reason from first principles and developing autonomous coding agents for real repositories. Key responsibilities include solving reward modeling problems, scaling laws for RL compute, and progressive alignment. Required skills include deep learning expertise, Python, and proficiency in JAX, PyTorch, or Tensorflow. Preferred experience includes RLHF, GRPO, PPO, RLVR, reward modeling, distillation, and managing long-horizon tasks with sparse attention or context compression.
Train foundation models for reasoning, coding, and agentic capabilities using reinforcement learning.
Develop autonomous coding agents capable of operating within real software repositories.
Build agentic systems that manage multi-step workflows with error recovery and tool-use planning.
Implement RL with verifiable rewards for mathematical reasoning and multi-turn coding tasks.
Research scaling laws for RL compute allocation and progressive alignment across capability stages.
Train models to manage their own context for long-horizon tasks and long context windows.
Optimize models specifically for Apple silicon to ensure private and integrated OS experiences.
Apply distillation techniques to combine independently optimized capabilities into single models.
What we're looking for
PhD or equivalent practical experience in Computer Science or a related technical field.
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.
Reinforcement learning for LLMs including RLHF, GRPO, PPO, RLVR, reward modeling, and RL scaling laws (preferred).
Experience with code generation, coding agents, and repository-level code understanding (preferred).
Experience with agentic systems, multi-turn RL, tool-use planning, and long-horizon task execution (preferred).
Experience with distillation, alignment, long context, and efficiency techniques (preferred).
Python
PyTorch
JAX
TensorFlow
LLM
Multi-modal LLM
Deep Learning
Synthetic Data
Reward Modeling
Preference Learning
Agentic Systems
Data Pipelines
Data Curation
Python
PyTorch
JAX
TensorFlow
Deep Learning
Large Language Models
Reinforcement Learning
Natural Language Processing
Speech Recognition
On-policy Distillation
Foundation Models
Reward Modeling
Voice Models
LLMs
Reinforcement Learning
Supervised Fine-Tuning
Python
PyTorch
JAX
Deep Learning
Distributed Training
Reward Modeling
Synthetic Data Generation
Data Filtering
Curriculum Learning
Model Steering
Preference Optimization
Python
PyTorch
JAX
TensorFlow
Deep Learning
Large Language Models
Reinforcement Learning
Natural Language Processing
Speech Recognition
On-policy Distillation
Foundation Models
Reward Modeling
Voice Models