Applied Machine Learning Engineer, Platform Architecture

Apple • Cupertino, CA

5d

About The Position

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.

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.

Requirements

• 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
• Experience in modern deep learning frameworks

Nice To Haves

• 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

Responsibilities

• 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
• Seeing those changes through to ship
• Building systems that drive architectural exploration and tuning for current and future Apple SoCs
• Targeting 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior