Applied Machine Learning Engineer, Platform Architecture

Join the SoC Architecture team building ML and generative AI systems that shape how future Apple silicon is architected and tuned. We're looking for an AI/ML Engineer who can turn complex hardware data into architectural insight. You will apply that expertise to studying and improving the performance and power behavior of modern System-on-Chip designs across the full product lifecycle: ML-driven research to identify what should change in hardware or software, hands-on partnership with silicon and OS teams to implement and bring those changes up on real silicon, and seeing them through to ship. This role is ideal for a hands-on ML engineer who is energized by both research and shipping product, and who thrives at the intersection of large-scale data, system architecture, and ML.

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.