This role pays more than
81%
of similar roles. Most pay
$196,350–$254,750
— the shaded band above.
At the midpoint, this role pays about
$255k
versus about
$226k
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.
As a Senior Machine Learning Engineer, Proactive, you will join the team building next-generation intelligent search and AI experiences that understand user intent and context while preserving privacy. You will design, train, fine-tune, and optimize transformer-based language models and foundation models for efficient on-device deployment. Your daily work involves building semantic retrieval, embedding, reranking, and retrieval-augmented generation systems to improve search quality. You will develop models for query understanding, intent prediction, and personalization while researching techniques like knowledge distillation, model compression, quantization, and pruning to meet strict memory and power constraints. Utilizing Python or C/C++ with frameworks like PyTorch, JAX, or TensorFlow, you will translate research into production-quality solutions. The role focuses on the technical challenge of adapting large foundation models into compact, high-performing systems for proactive, personalized intelligence across a complex ecosystem.
What does a Machine Learning Engineer earn in California?
Median $246150 from 187 postings across 29 companies.
Design and optimize transformer-based language models and foundation models for efficient on-device deployment.
Build semantic retrieval, embedding, reranking, and retrieval-augmented generation systems to improve search quality.
Develop models for query understanding, intent prediction, personalization, and ranking.
Implement model compression techniques including knowledge distillation, quantization, pruning, and low-latency inference.
Create scalable experimentation and evaluation pipelines to measure model quality, robustness, and performance metrics.
Research and prototype new applications for multimodal AI, agentic retrieval, and personalized intelligence.
Translate research advances into production-quality machine learning solutions while balancing memory, power, and latency constraints.
What we're looking for
Master degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
5+ years of industry or research experience developing machine learning systems.
Background in machine learning, deep learning, NLP, information retrieval, search, recommender systems, or generative AI.
Experience training, fine-tuning, and deploying transformer-based models and large language models.
Experience with modern deep learning architectures including transformers, embeddings, representation learning, and neural ranking.
Proficiency in Python and/or C/C++ using frameworks like PyTorch, JAX, or TensorFlow to build production-quality software.
Experience optimizing models for resource-constrained environments, such as knowledge distillation, compression, quantization, and pruning (preferred).
Experience building RAG systems, vector search, neural reranking, or semantic search systems (preferred).