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.