Machine Learning Engineer, Data Solutions Program (ADSP)

Apple Inc.; Austin (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

Apple Inc. is seeking candidates for the Applied Data Solutions Program in Austin, Texas. The role focuses on building scalable data products, pipelines, and tooling to enable rapid business intelligence and data science across Apple’s ecosystem.

Responsibilities include deploying machine-learning models, collaborating with ML and software engineers, and translating business problems into technical solutions while driving fraud mitigation, security improvements, and operational insights.

Qualifications

• Undergraduate or graduate degree in Computer Science, Data Science, Data Analytics, Machine Learning or related field.
• Proficient in at least one programming language (Python/Scala/Java).
• Practical experience deploying machine learning solutions to answer real-world questions.

Responsibilities

• Deliver data-driven solutions that mitigate fraud, improve security, and optimize customer experiences.
• Collaborate with ML and software engineers to design scalable, user-friendly systems and tools.
• Translate ambiguous business problems into technical requirements and actionable plans.
• Develop self-service tooling and automation to improve data engineering efficiency and analytics.
• Build and monitor ML models for real-time and forensic threat actions within Apple’s ecosystem.

Skills

Python

Scala

Java

Data analysis

Machine Learning

Communication

Education

Degree in Computer Science / Data Science / Data Analytics / ML

Tools

Dataiku

Ray

GPU-based training

Job description

Apple Inc. is seeking candidates for the Applied Data Solutions Program in Austin, Texas. The role focuses on building scalable data products, pipelines, and tooling to enable rapid business intelligence and data science across Apple’s ecosystem.

Responsibilities include deploying machine-learning models, collaborating with ML and software engineers, and translating business problems into technical solutions while driving fraud mitigation, security improvements, and operational insights.