Back to search
Description
You will join a multidisciplinary team of ML, software, and architecture engineers building systems that drive architectural exploration and tuning for current and future Apple SoCs. The work targets the full performance-power tradeoff space across fabric, memory subsystem, system caches, dynamic voltage and frequency state control, clock and power gating policies, sleep state controls, and bottleneck prevention.
Minimum Qualifications
B.S. in Computer Science, Computer Engineering, Electrical Engineering, or a 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 experience in modern deep learning frameworks.
Preferred Qualifications
Working knowledge of SoC compute, memory, and power-management subsystems, how real workloads exercise them, and the C/C++ modeling infrastructure typical of SoC environments. Depth in time-series analysis, including feature engineering on streaming telemetry. Experience applying ML beyond prediction, including driving decisions, optimizing policies, and efficiently searching large configuration spaces. Track record of training large-scale models across distributed clusters. Experience building stateful, multi-turn agentic frameworks and complex execution flows. M.S. or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related field and 10+ years of relevant experience. Track record of moving quickly from hypothesis to result and iterating based on evidence. Excellent communication and collaboration skills to work effectively across technical disciplines.
Learn more about this Employer on their Career Site
