This role pays more than
77%
of similar roles. Most pay
$202,800–$254,750
— the shaded 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
2305 open roles
on FindRole.
Listed pay typically runs
$175,000–$280,000
across 1873 roles with salary data.
AIML - Machine Learning Engineer, Foundation Models joins a team developing sophisticated foundation models that power intelligent features across the product ecosystem. This role involves working across the full training lifecycle, including pre-training and mid-training approaches to bridge general capability with task-specific performance. The engineer will design and implement scalable, high-performance machine learning infrastructure for text, image, speech, and multi-modal domains while optimizing models for performance, efficiency, and on-device intelligence. Key responsibilities include productionizing state-of-the-art algorithms and solving problems in reward modeling and alignment. Required skills include expert Python programming and proficiency with Jax, PyTorch, or TensorFlow. The role requires expertise in distributed training, model optimization, and infrastructure using Kubernetes, Docker, and cloud platforms like AWS, GCP, or Azure to solve complex challenges regarding privacy, security, and integration into the operating system.
What does a Machine Learning Engineer earn in New York?
Median $231150 from 51 postings across 14 companies.
Design and implement scalable, high-performance machine learning infrastructure for multi-modal foundation models.
Develop mid-training approaches to bridge general capabilities with task-specific performance.
Optimize models specifically for Apple silicon to improve performance and efficiency on-device.
Implement machine learning systems that adhere to strict privacy and security requirements.
Solve complex reward modeling problems to prevent reward hacking in agentic settings.
Align models across a spectrum of creative tasks and precise, action-taking workflows.
Transform research prototypes into production-ready AI solutions for the product ecosystem.
Manage a small team of engineers as required by project needs.
What we're looking for
MS or PhD in Computer Science, Machine Learning, or a related technical field.
Expert-level programming skills in Python.
Proficiency in machine learning frameworks such as Jax, PyTorch, and TensorFlow.
Strong background in distributed training, model optimization, and machine learning infrastructure.
Experience with large-scale model training and deployment.
Familiarity with Kubernetes, Docker, cloud platforms (AWS, GCP, Azure), and distributed computing frameworks.
Experience with foundation models, large language models, or multi-modal AI systems (preferred).
Demonstrated ability to transform research prototypes into production systems, published research, or significant open-source contributions (preferred).
Python
PyTorch
Jax
TensorFlow
Foundation Models
Large Language Models
Distributed Training
Model Optimization
Kubernetes
Docker
AWS
GCP
Azure
Multi-modal AI
Machine Learning Infrastructure
Python
PyTorch
Jax
TensorFlow
Distributed Training
Large Language Models
Foundation Models
Model Optimization
Kubernetes
Docker
AWS
GCP
Azure
Multi-modal AI
Machine Learning Infrastructure
On-device 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
Python
PyTorch
JAX
TensorFlow
Deep Learning
Foundation Models
Large Language Models
Reinforcement Learning
Vision-Language Modeling
Video Generation
Data Pipelines
Reward Modeling
On-policy Distillation
Multimodal Models
Deep Learning
Python
PyTorch
JAX
TensorFlow
Natural Language Processing
Multi-modal Understanding
Information Retrieval
Foundation Models
Machine Learning
LLMs
Supervised Fine-Tuning (SFT)
Reinforcement Learning
Python
PyTorch
JAX
Deep Learning
Distributed Training
Reward Modeling
Synthetic Data Generation
Data Filtering
Curriculum Learning
Algorithm Research