ML Engineer, Platform Architecture: SoC Power & Performance

Apple Inc.; Cupertino, Northern (CA, KY); Hybrid; USD 185,000 - 278,000; Full time

Job summary

Apple Inc. in Cupertino, California seeks an Applied Machine Learning Engineer to turn hardware data into architectural insight for Apple silicon. You will study performance and power across SoC designs, partnering with silicon and OS teams to implement changes on real silicon and guide them to ship.

You join a multidisciplinary team building ML and AI workflows for architectural exploration, spanning time-series telemetry, performance-per-watt optimization, and the full product lifecycle.

Qualifications

• BS in Computer Science, Computer Engineering, Electrical Engineering, or related field.
• Applied ML industry experience deploying complex ML systems in production.
• Experience applying modern ML techniques to large real-world datasets.
• Programming experience in Python and modern DL frameworks.

Responsibilities

• Design and build advanced ML and generative AI workflows to automate workload analysis and SoC architectural exploration.
• Apply modern deep learning and generative AI techniques to large-scale workload data to inform software and hardware improvements.
• Translate insights into concrete hardware and software changes to improve performance and power.
• Partner with hardware, software, and architecture teams to implement changes and ship.

Skills

Applied ML production deployment

Large real-world datasets

Python programming

Deep learning frameworks

ML research to production cycle

Communication and collaboration

Time-series analysis

ML and AI system design

C/C++ modeling understanding

Education

Bachelor's degree in CS/CE/EE or related

MS/PhD with 10+ years experience

Tools

C/C++ modeling infrastructure

Job description

Apple Inc. in Cupertino, California seeks an Applied Machine Learning Engineer to turn hardware data into architectural insight for Apple silicon. You will study performance and power across SoC designs, partnering with silicon and OS teams to implement changes on real silicon and guide them to ship.

You join a multidisciplinary team building ML and AI workflows for architectural exploration, spanning time-series telemetry, performance-per-watt optimization, and the full product lifecycle.