Applied Machine Learning Engineer, Platform Architecture

What you'll do

• Design and build advanced ML and generative AI workflows to automate workload analysis.
• Apply deep learning techniques to large-scale time-series telemetry to inform architectural improvements.
• Translate data insights into concrete hardware and software changes for performance and power optimization.
• Partner with silicon and OS teams to implement and validate changes on real silicon.
• Manage the full product lifecycle from ML-driven research to shipping final hardware and software.
• Optimize performance-power tradeoffs across fabric, memory subsystems, and cache systems.
• Train large-scale models across distributed clusters to drive architectural decisions.
• Build stateful, multi-turn agentic frameworks and complex execution flows.

What we're looking for

• 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 modern deep learning frameworks.
• Working knowledge of SoC compute, memory, and power-management subsystems and C/C++ modeling infrastructure (preferred).
• Depth in time-series analysis, including feature engineering on streaming telemetry (preferred).
• Experience applying ML for driving decisions, optimizing policies, and searching large configuration spaces (preferred).
• Track record of training large-scale models across distributed clusters (preferred).