CAD/Flow Methodology Development

About Tylsemi
Tylsemi is building and scaling high-impact semiconductor operations. We partner across design, manufacturing, and supply chain to bring silicon from concept to high-volume production with speed, quality, and predictable execution. Role Overview
As a CAD/Flow Methodology Development leader at Tylsemi, you will define, build, and scale the EDA infrastructure and design signoff methodologies that enable teams to deliver silicon efficiently and repeatably.

This role will be good for 10+ years of experience and is ideal for engineers who combine deep tool/flow expertise with strong cross-functional leadership—turning ambiguous productivity and quality goals into robust, automated, measurable flows used across multiple programs and sites. What You’ll Do
Own end-to-end methodology for key implementation and signoff flows (front-end through back-end), with clear quality gates, metrics, and release processes

Develop and maintain automated flows for synthesis, STA, P&R, physical verification (DRC/LVS), extraction, EM/IR, timing/power closure, and signoff reporting (scope aligned to your background)

Build reusable infrastructure: Makefiles/Python/Tcl-based automation, job orchestration, regression frameworks, dashboards, and standardized runsets

Drive tool enablement and configuration across EDA vendors (e.g., Synopsys/Cadence/Siemens), including version qualification, best-known methods, and performance tuning

Partner with design, PD, verification, DFT, packaging, and product engineering to define signoff criteria, checklists, and tapeout readiness reviews

Create scalable collateral: flow documentation, onboarding guides, templates, reference designs, and training for new teams and new nodes

Debug and root-cause flow issues (tool bugs, runset problems, environment drift, corner setup, library/constraint issues) and drive fixes to closure

Establish and track KPIs (runtime, QoR, convergence rate, violation trends, tapeout escapes) and continuously improve cycle time and first-pass success

Support multi-site execution with reproducible environments, access control, compute strategy, and robust release management

What We’re Looking For
10+ years of experience in CAD, design methodology, or physical design/STA with significant flow ownership (scope aligned to experience level)

Strong understanding of digital implementation and signoff concepts: constraints (SDC), timing closure, CDC/RDC considerations, power intent (UPF/CPF), physical verification, extraction, and signoff correlation

Proven ability to build production-grade automation and infrastructure (Python, Tcl, shell, Make/CMake, Git-based workflows; CI/regressions a plus)

Experience qualifying and supporting EDA tools and flows across multiple projects, including versioning, runset management, and reproducibility

Ability to translate engineering pain points into scalable methodology, with clear documentation and measurable outcomes

Strong debugging skills across tool logs, QoR deltas, environment issues, and cross-team integration problems

Clear communication and high ownership in cross-site, cross-functional environments

Required Skills
EDA CAD/flow development

Design methodology and signoff flows

Automation/scripting (Python/Tcl/shell)

Tool enablement and regression infrastructure

Cross-functional technical leadership

Nice to Have
Hands-on experience with advanced-node considerations (multi-corner/multi-mode scale, variation, DFM, reliability signoff, foundry collateral integration)

Experience with compute/grid orchestration (LSF/SLURM/Kubernetes), containerized environments, and reproducible builds

Flow integration for formal/CDC, low-power verification, DFT signoff, or mixed-signal/AMS-adjacent signoff needs

Experience building QoR/runtimes dashboards and automated tapeout readiness reporting

Prior ownership of tapeout-critical signoff checklists and release processes across multiple programs

Success in This Role Looks Like
Teams run consistent, reproducible flows with clear signoff criteria and minimal manual intervention

Tapeout readiness is predictable: fewer late surprises, faster convergence, and strong correlation across tools and stages

Automation and regressions catch issues early (environment drift, runset changes, library/constraint problems) before they impact schedules

Cycle time improves measurably through runtime optimization, standardized methodology, and reusable collateral

Cross-functional partners trust the flow: documentation is clear, support is responsive, and releases are well-managed

Location
Bengaluru, India

San Jose, CA

Experience
10+ years (scope and ownership will be aligned to experience level).