ML Engineer — Strategic Data Solutions (Real-Time Impact)

Apple Inc.; Austin (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

Apple Inc. in Austin, TX is seeking a Machine Learning Engineer to help solve complex problems with data-driven solutions that impact products and customer experiences. You will work with cross-functional teams to define, build, deploy, and maintain end-to-end solutions that improve security, efficiency, and fraud mitigation.

The role emphasizes collaboration, learning, and mentorship within a supportive SDS team, with opportunities to push the boundaries of data science methods at scale.

Qualifications

• Degree in CS, Statistics or related field, or equivalent experience.
• Experience deploying ML solutions to real-world questions.
• Experience programming in Python, Scala, or Java.

Responsibilities

• Engage with stakeholders to translate business problems into technical solutions.
• Design data science-driven solutions balancing traditional and custom approaches.
• Collaborate to implement real-time and batch decisioning in production.
• Create reporting and monitor decisioning quality for metrics health.
• Investigate trends and adapt models with agile logic changes.
• Communicate with stakeholders of varying technical backgrounds about work.
• Share learnings on novel technologies to boost team capabilities.

Skills

Data science techniques

Python/Scala/Java

Real-time & batch processing

Reporting & monitoring

Stakeholder communication

Education

Graduate degree in CS/Statistics/related

Bachelor’s with equivalent experience

Tools

SQL

Job description

Apple Inc. in Austin, TX is seeking a Machine Learning Engineer to help solve complex problems with data-driven solutions that impact products and customer experiences. You will work with cross-functional teams to define, build, deploy, and maintain end-to-end solutions that improve security, efficiency, and fraud mitigation.

The role emphasizes collaboration, learning, and mentorship within a supportive SDS team, with opportunities to push the boundaries of data science methods at scale.