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 machine learning infrastructure for text, image, speech, and multi-modal domains while optimizing models for performance, efficiency, and on-device intelligence. Key technical requirements include expert Python skills and proficiency in Jax, PyTorch, or TensorFlow. Candidates should possess experience in distributed training, model optimization, Kubernetes, Docker, and various cloud platforms. The work focuses on solving frontier problems like reward modeling to resist hacking, handling sparse rewards in agentic settings, and aligning models for both creative tasks and precise action-taking workflows while meeting stringent privacy requirements.
What does a Machine Learning Engineer earn in California?
Median $246150 from 187 postings across 29 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 reward modeling techniques to prevent reward hacking in agentic settings.
Align models across creative tasks and precise, action-taking workflows.
Transform research prototypes into production-ready AI systems.
Develop machine learning systems that meet stringent privacy and security requirements.
Manage a small team of engineers as required.
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
Distributed Training
Model Optimization
Kubernetes
Docker
AWS
GCP
Azure
Foundation Models
Large Language Models
Multi-modal AI
Machine Learning Infrastructure
On-device Machine Learning
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
On-policy Distillation
Multimodal Models
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
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