This role pays more than
85%
of similar roles. Most pay
$192,050–$254,750
— the shaded band above.
At the midpoint, this role pays about
$255k
versus about
$223k
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, Visual Intelligence Technology will join the Visual Intelligence Technology team to develop user experiences powered by Apple Intelligence. The role involves exploring how large-scale models can understand and reason about complex visual scenes while designing and building large-scale training pipelines for experimentation at scale. Collaborating with researchers and data scientists, you will develop, fine-tune, and evaluate domain-specific LLMs for various applications and conduct applied research to transition generative AI into production-ready technologies. Key responsibilities include advancing technology in Visual Search, Multimodal Foundation Models, and features like Visual Look-up. Required skills include expertise in machine learning, deep learning, information retrieval, and natural language processing. Candidates should be proficient with software packages such as Keras, TensorFlow, or PyTorch, and possess experience in prompt engineering and data collection tooling for LLMs.
What does a Machine Learning Engineer earn in California?
Median $246150 from 187 postings across 29 companies.
Develop and fine-tune domain-specific Large Language Models (LLMs) for various product applications.
Design and build large-scale training pipelines for multimodal foundation models.
Conduct experiments at scale to improve how models understand and reason about complex visual scenes.
Translate groundbreaking generative AI research into production-ready technologies.
Develop tools for data collection, annotation, and management specifically for LLMs.
Implement advanced features like Visual Search and Visual Look-up in camera and photo applications.
Design machine learning platforms, architectures, and algorithms to power user-facing intelligence.
What we're looking for
MS or PhD in Computer Science, Artificial Intelligence, Machine Learning, Information Retrieval, Data Science, or a related field.
5+ years of experience in machine learning, deep learning, information retrieval, natural language processing, or data mining.
Experience as a tech lead successfully leading a team to ship software to users.
Excellent knowledge and practical skills in designing ML platforms, architectures, or algorithms.
Strong interpersonal skills with the ability to work independently and in a team.
3+ years of experience in prompt engineering, fine-tuning, evaluating, and developing data collection/annotation/management tooling for LLMs (preferred).
Experience with relevant deep learning software packages such as Keras, TensorFlow, or PyTorch (preferred).
LLM
Machine Learning
Deep Learning
Natural Language Processing
PyTorch
TensorFlow
Keras
Prompt Engineering
Fine-tuning
Information Retrieval
Data Mining
Generative AI
ML Platforms
Machine Learning
Deep Learning
LLM
Natural Language Processing
Generative AI
PyTorch
TensorFlow
Keras
Prompt Engineering
Information Retrieval
Word Embeddings
Metal
OpenGL
OpenCL
Vulcan
iOS
macOS
Data Mining
Machine Learning
Deep Learning
LLM
Natural Language Processing
Generative AI
PyTorch
TensorFlow
Keras
Prompt Engineering
Information Retrieval
Data Mining
Metal
OpenGL
OpenCL
Word Embeddings
iOS
macOS
Large Language Models
LLMs
Natural Language Processing
Python
PyTorch
TensorFlow
JAX
Deep Learning
Generative AI
RAG
Prompt Engineering
RLHF
DPO
PPO
LoRA
Supervised Fine-tuning
Recommendation Systems
Machine Learning
Computer Vision
Natural Language Processing
Python
PyTorch
Jax
Generative AI
Multi-modal LLM
Synthetic Data Generation
Data Engineering
Foundation Models
Model Evaluation Pipelines
C++
Python
GPU Programming
Machine Learning
Swift
Numerical Computing
Distributed Training
Software Engineering
Low-level Programming
MLX
Apple Silicon