At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
Key Responsibilities
Automation Framework Ownership
- 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.
- 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.
Performance and Power Collateral Generation
- 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.
- Validate result quality, consistency, and reproducibility before collateral is shared with stakeholders.
Team Enablement and Support
- Serve as the primary internal point of contact for users of the COLLATERAL-AUTO project.
- Onboard new users and provide guidance on running the XNNC, Palladium, and Joules workflow.
- Debug user runs and triage issues related to environments, scripts, CAD/EDA tools, and flow configuration.
- Review performance and power summaries for correctness, completeness, and consistency.
- Improve team-wide productivity by creating reusable guidance, troubleshooting documentation, and reliable reference workflows.
Required Qualifications
Experience
- 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.
Technical Expertise
- 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.
Education
- Bachelor’s or Master’s degree in Software Engineering, Electronics Engineering, Electrical Engineering, Computer Engineering, or a related discipline.
Preferred Qualifications
- 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.
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