Principal Engineer, Automated Derivatives
Core
Lead end-to-end delivery of derivative System-on-Chips (SoCs) by building an AI-augmented 'Silicon Factory' that uses machine learning to bridge architectural intent and GDSII, focusing on ultra-fast turnaround times.
Role type
Principal Engineer, Automated Derivatives (Hardware/ML)
Builds
Derivative SoCs, RTL wrappers, memory maps, bus interconnects, and physical implementations
Domain
Semiconductor / Hardware Design / Machine Learning
Deliverable
production ML models | product features
Required skills
SystemVerilog, UVM, Python, Tcl, Static Timing Analysis (STA), Physical Design (Synthesis, P&R), Generative AI, CI/CD pipelines
Preferred skills
Experience with simulation tools (VCS, Xcelium), implementation tools (Innovus, ICC2), data-driven flow development
Technologies
SystemVerilog, UVM, Python, Tcl, Git, Jenkins, GitLab, VCS, Xcelium, Innovus, ICC2
Responsibilities
Develop scripts and Generative AI prompts to automate RTL wrapper creation; Build AI-driven verification environments that adjust constraints and coverage goals; Deploy pattern-recognition models to identify bug-prone modules; Drive physical implementation using AI to reuse placement and routing solutions; Use ML-based predictors to evaluate RTL code for timing and congestion bottlenecks
Seniority
Principal, hands-on IC