Principal Software Engineer

• Own, maintain, and enhance the end-to-end automation flow used to:
• Compile neural networks using the XNNC compiler.
• Run generated RTL binaries on Palladium Z3 emulation.
• Drive Joules gate-level power analysis.

• Develop and maintain the Python, CSH, and Bash scripts that integrate and orchestrate the flow.

• Develop and maintain the Python, CSH, and Bash scripts that integrate and orchestrate the flow.
• Add support for new AI network configurations, hardware accelerator variants, reference builds, and EDA tool revisions.
• Maintain current documentation, patches, reference configurations, and reproducible build instructions.
• Identify, debug, and resolve automation, infrastructure, and tool-integration issues across the workflow.

• Generate complete performance and power collateral for each neural network processed through XNNC.
• Collect and summarize:
• Cycle count, frames per second (FPS), and memory-bandwidth metrics from XTSC simulation.
• End-to-end performance metrics from Palladium emulation.
• Dynamic power estimates, including total and memory power, from Joules gate-level analysis.

• Deliver results consistently across reference builds in standard formats, including cycle-count CSVs, power CSVs, and consolidated summary reports.

• Deliver results consistently across reference builds in standard formats, including cycle-count CSVs, power CSVs, and consolidated summary reports.
• Validate result quality, consistency, and reproducibility before collateral is shared with stakeholders.

• 10+ years of experience in various embedded software or general software development roles.

• 10+ years of experience in various embedded software or general software development roles.
• 3+ years of experience with AI frameworks, AI network compilation, or related tooling.
• Experience with one or more of: TensorFlow, PyTorch, ONNX, Apache TVM, or AI network compilers.

• Strong Python scripting skills.

• Strong Python scripting skills.
• Proficiency with CSH and Bash shell scripting.
• Working knowledge of JSON and automation-oriented configuration formats.
• Understanding of hardware edge-AI accelerators, RTL execution flows, and performance analysis.
• Familiarity with debugging complex software, toolchain, and multi-stage automation issues.

• Bachelor’s or Master’s degree in Software Engineering, Electronics Engineering, Electrical Engineering, Computer Engineering, or a related discipline.

• Bachelor’s or Master’s degree in Software Engineering, Electronics Engineering, Electrical Engineering, Computer Engineering, or a related discipline.

• Experience using AI-assisted development tools for rapid prototyping and effective technical prompting.

• Experience using AI-assisted development tools for rapid prototyping and effective technical prompting.
• Experience with Cadence tools, including Palladium and Joules, or equivalent EDA/emulation/power-analysis workflows.
• Familiarity with IP design platforms, RTL simulation or emulation, gate-level power analysis, and AI accelerator validation.
• Strong written communication skills for producing technical documentation, runbooks, and performance/power reports.