ML-Powered Stability Engineer for Embedded Systems

Groupe Kardol; Austin (TX); On-site; USD 127,000 - 221,000; Full time

Benefits offered by this job

Apple benefits

Stock programs

Tuition reimbursement

Job summary

Apple in Austin seeks a Software Engineer to contribute to Mac and Home stability test coverage, building automated frameworks to handle thousands of test hours weekly. You will work at the nexus of hardware and software, designing ML-powered validation tools and collaborating across engineering and QA teams.

You will analyze data, develop ML-assisted failure analysis, and help prevent issues before customers are affected.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or Electrical Engineering.
• 2 years experience involving machine learning algorithms and their practical applications.
• Hands-on programming experience with Python.
• Exposure to one or more ML frameworks (PyTorch, TensorFlow, or JAX).
• Familiarity with software engineering fundamentals (version control, testing, debugging).
• Excellent written and verbal communication skills.

Responsibilities

• Develop key new areas of Mac and Home stability test coverage enhanced by ML capabilities.
• Balance day-to-day automation on thousands of units daily.
• Design and implement automated stress and stability tests for embedded systems.
• Contribute to ML-powered test automation frameworks and validation tools.
• Analyze validation/test data and help build predictive models that detect issues early.
• Collaborate with cross-functional engineering and QA teams.

Skills

Python

ML algorithms

Swift

macOS development

Data visualization

Tableau

ML frameworks

Education

Bachelor's degree in Computer Science, Computer Engineering, or Electrical Engineering

Tools

CoreML

Xcode

Superset

Tableau

Job description

Apple in Austin seeks a Software Engineer to contribute to Mac and Home stability test coverage, building automated frameworks to handle thousands of test hours weekly. You will work at the nexus of hardware and software, designing ML-powered validation tools and collaborating across engineering and QA teams.

You will analyze data, develop ML-assisted failure analysis, and help prevent issues before customers are affected.