This role pays less than
55%
of similar roles. Most pay
$202,800–$254,750
— the shaded 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
1984 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1590 roles with salary data.
As an ML Engineer, Apple Foundation Models, you will join the team shaping the data foundation and intelligence for frontier models. You will develop data strategies, pipelines, and methodologies across the full training lifecycle, including pre-training, mid-training, and post-training. Your daily work involves building scalable systems for data curation and quality assessment for text, multimodal, reasoning, and agentic training data. You will create synthetic data pipelines, model self-improvement frameworks, and data flywheels to improve performance in areas like planning, coding, and tool use. The role requires proficiency in Python and deep learning toolkits such as JAX, PyTorch, or TensorFlow. You will solve complex problems regarding reward modeling, preference learning, and scalable alignment to advance frontier capabilities in reasoning and multimodal understanding for next-generation intelligent experiences.
Drive data strategy and mixture design across the pre-training, mid-training, and post-training lifecycles.
Build scalable systems for generating, curating, and assessing text, multimodal, reasoning, and agentic training data.
Develop synthetic data pipelines to teach models complex capabilities like planning, coding, and tool use.
Create self-improvement frameworks where foundation models generate and refine their own training data.
Establish data flywheels that convert model feedback and user interactions into high-quality training signals.
Use benchmark-driven methodologies to identify capability gaps and implement targeted data interventions.
Advance state-of-the-art techniques in reward modeling, preference learning, and scalable alignment for foundation models.
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 of applying deep learning to products.
Proficiency in Python and at least one deep learning toolkit such as JAX, PyTorch, or TensorFlow.
Experience developing data-centric solutions for foundation models, specifically large-scale data flywheels.
Experience building agentic systems, tool-use capabilities, and reasoning models.
Experience with model self-improvement techniques and multimodal foundation models across text, vision, audio, and video.
Experience using user interaction data or real-world feedback while maintaining privacy and data governance standards.
LLM
Reinforcement Learning
Python
PyTorch
JAX
Distributed Training
Deep Learning
Data Filtering
Curriculum Learning
Model Steering
Preference Optimization
Synthetic Data Generation
Machine Learning
Python
PyTorch
JAX
TensorFlow
Deep Learning
Foundation Models
Large Language Models
Reinforcement Learning
Vision-Language Modeling
Video Generation
Data Pipelines
Reward Modeling
Multimodal Models
On-policy Distillation