Physical Design Engineer, Timing
Austin, United States · Hybrid · Full-time
- Posted 2w ago
- From Tenstorrent’s careers page
- Location
- Austin, United States
- Work mode
- Hybrid
- Type
- Full-time
- Level
- Senior
- Experience
- 8+ years
- Department
- Engineering
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About the role
Who You Are
- A physical design engineer with 8+ years of experience in STA, timing closure, and advanced-node design implementation.
- Experienced with timing flows, constraints, clocking, CDC, RDC, derates, uncertainties, guardbanding, and final signoff.
- Skilled in writing and debugging SDC constraints, creating ECOs, developing closure strategies, and analyzing worst-case corners.
- A strong collaborator and communicator who can drive results across distributed, cross-functional engineering teams.
What We Need
- Drive timing convergence across blocks, partitions, sections, and top-level designs; generate and validate timing models and publish results.
- Analyze timing violations, develop and propagate fixes, debug timing-flow issues, and keep execution progressing through tapeout.
- Develop and validate bottom-up constraints while partnering with Design and DFT teams on methodology updates and closure requirements.
- Use Python, Perl, and Tcl for automation and flow development; experience with SPICE, PNR, placement, CTS, routing, post-route optimization, DFT, or AI-based automation is a plus.
What You Will Learn
- How to develop scalable timing models, constraints, and closure strategies across multiple levels of design hierarchy.
- How clocking, guardbanding, derates, uncertainties, CDC, RDC, and process corners influence timing convergence.
- How timing, physical design, Design, and DFT methodologies come together to deliver successful tapeouts.
- How to improve implementation and signoff workflows through scripting, automation, flow development, and emerging AI-enabled solutions.
Skills they ask for
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About Tenstorrent
Compute for every scaleTenstorrent develops AI computing systems, including superclusters and workstations for running AI workloads.
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