below marketmost similar roles pay hereabove market
This role pays less than
55%
of similar roles. Most pay
$202,800–$254,750
— the blue band above.
At the midpoint, this role pays about
$214k
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 Engineer, Apple Foundation Models joins the Apple Foundation Model team to shape the data foundation and intelligence for frontier models. The role involves developing data strategies, pipelines, and methodologies across the full training lifecycle, including pre-training, mid-training, and post-training. You will build scalable data generation, curation, and quality assessment systems for text, multimodal, reasoning, and agentic training data. Key responsibilities include creating synthetic data pipelines, model self-improvement frameworks, and data flywheels to improve model performance. You will utilize Python and deep learning toolkits such as JAX, PyTorch, or Tensorflow. The work focuses on solving technical problems related to reasoning, planning, coding, tool use, and multimodal understanding while advancing state-of-the-art techniques in reward modeling, preference learning, and scalable alignment for foundation models.
Drive data strategy and mixture design across the pre-training, mid-training, and post-training lifecycles.
Build scalable systems for data generation, curation, and quality assessment for text and multimodal data.
Develop synthetic data pipelines to teach models reasoning, planning, coding, and tool use.
Create frameworks for model self-improvement to generate and refine training data automatically.
Pioneer data flywheels that convert model feedback and user interactions into high-quality training signals.
Develop benchmark-driven methodologies to identify capability gaps and diagnose model failure modes.
Implement state-of-the-art techniques in reward modeling, preference learning, and scalable alignment.
What we're looking for
Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.
Demonstrated expertise in LLM or Multi-modal LLM with a publication record in relevant conferences 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.
Experience developing data-centric solutions for foundation models, especially large-scale data flywheels (preferred).
Experience improving foundation models using user interaction data, private data, or other real-world feedback signals while maintaining privacy and data governance standards (preferred).
Experience building agentic systems, tool-use capabilities, and reasoning models (preferred).
Experience with model self-improvement techniques (preferred).