Principal Engineer, Automated Derivatives
HybridAustin, Texas, United States
Job Summary
Lead end-to-end delivery of derivative SoCs by building an AI-augmented "Silicon Factory" that uses machine learning to bridge architectural intent and GDSII. Implement ML-based predictors to evaluate RTL code for timing and congestion bottlenecks before synthesis, and develop scripts to automate the creation of RTL wrappers, memory maps, and bus interconnects. Construct AI-driven verification environments that automatically adjust constraints and coverage goals, while deploying pattern-recognition models to identify bug-prone modules based on complexity metrics. Ensure seamless handoff from RTL to physical design by generating floorplan constraints and timing assertions, and drive physical implementation by reusing placement and routing solutions from parent designs to achieve faster convergence.
Required Qualifications
- Minimum of a Master's degree in Electrical Engineering, Computer Science, or Computer Engineering
- 12–15 years of professional experience in the semiconductor industry
- Proficiency in SystemVerilog for RTL design
- Proficiency in UVM for functional verification
- Solid understanding of Synthesis, P&R, and STA (Static Timing Analysis)
- Expert Python skills to build and deploy models that interface with both simulation tools (VCS, Xcelium) and implementation tools (Innovus, ICC2)
- Experience using Tcl/Python to extract features from simulation logs and implementation reports to train predictive models
- Mastery of Git and CI/CD pipelines (Jenkins/GitLab)
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