ML Engineer, Search & Knowledge Quality

Apple Inc.; Seattle (WA); On-site; USD 150,400 - 277,600; Full time

Benefits offered by this job

Stock options

Medical and dental coverage

Relocation assistance

Education reimbursement

Job summary

Apple Inc. is seeking a Machine Learning Engineer to push the boundaries of information access across devices.

You will own end-to-end ML features in the Siri Search system, collaborating with data scientists, software and ML engineers, and product managers to turn research into scalable production solutions. You'll work with state-of-the-art LLMs and multimodal techniques, focusing on query understanding, document ranking, and high-quality answer generation across geographies.

Qualifications

• BSc or Masters degree in Machine Learning, Data Science, Computer Science, Information Security, Mathematics, Statistics, or related field.

Responsibilities

• Design, develop, and implement machine learning models and RAG based agentic solution for NLP, and multimodal applications.
• Integrate and own ML solutions into production systems and existing workflows at scale.
• Collaborate with data scientists, software & ML engineers as well as product managers to define requirements and deliverables
• Independently own deliverables and apply problem-solving skills to identify solutions to problems to take ownership of complex, ambiguous problems and drive cross-functional solutions with minimal guidance.

Skills

Python

C/C++

GoLand

TensorFlow

PyTorch

HuggingFace

Scikit-learn

AXLearn

CI/CD

ML pipelines

Communication

Education

BSc or MSc in ML/CS/Data Science/Math/Stats

Tools

AXLearn

GoLand

Job description

Apple Inc. is seeking a Machine Learning Engineer to push the boundaries of information access across devices.

You will own end-to-end ML features in the Siri Search system, collaborating with data scientists, software and ML engineers, and product managers to turn research into scalable production solutions. You'll work with state-of-the-art LLMs and multimodal techniques, focusing on query understanding, document ranking, and high-quality answer generation across geographies.