This role pays more than
66%
of similar roles. Most pay
$192,050–$254,750
— the shaded band above.
At the midpoint, this role pays about
$242k
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. This role involves exploring how large-scale models can understand and reason about complex visual scenes while designing and building large-scale training pipelines and conducting experiments at scale. You will collaborate with researchers and data scientists to develop, fine-tune, and evaluate domain-specific LLMs for various applications in camera and photos features like Visual Look-up. The work involves advancing technology innovation in Multimodal Foundation Models and translating generative AI research into production-ready technologies. Required skills include expertise in machine learning, deep learning, information retrieval, and natural language processing. Preferred qualifications include experience with prompt engineering, fine-tuning LLMs, and using software packages such as Keras, TensorFlow, or PyTorch to solve complex visual search problems.
What does a Machine Learning Engineer earn in Washington?
Median $241750 from 71 postings across 15 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 techniques in prompt engineering and model evaluation.
Build and optimize machine learning architectures and algorithms for user-facing features like Visual Look-up.
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 who has successfully led 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 both independently and as part of 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
Data Mining
Information Retrieval
Generative AI
ML Platforms
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
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
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