Physical Design Engineer, Timing (AI)

Location: Hybrid, based in Austin, Texas; Fort Collins, Colorado; or Santa Clara, California, United States

Compensation: $100,000–$500,000, including base and variable compensation targets.

Company

Tenstorrent develops AI computing technology, including high-performance RISC-V CPUs and an integrated AI platform spanning software, compilers, platforms, networking, and semiconductors.

What you will do

• Drive timing convergence across blocks, partitions, sections, and top-level multi-million-gate designs.
• Analyze timing violations, debug timing-flow issues, develop fixes, and support execution through tapeout.
• Generate and validate timing models, publish results, and develop bottom-up timing constraints.
• Write and debug SDC constraints and create engineering change orders for timing closure.
• Partner with Design, DFT, and cross-functional engineering teams on methodology updates and final signoff.
• Develop automation and timing-flow improvements using Python, Perl, and Tcl.

Requirements

• 8+ years of experience in static timing analysis, timing closure, and advanced-node design implementation.
• Experience with timing flows, constraints, clocking, CDC, RDC, derates, uncertainties, guardbanding, and final signoff.
• Strong skills in SDC constraint development and debugging, ECO creation, closure strategy development, and worst-case corner analysis.
• Ability to collaborate and communicate effectively across distributed, cross-functional engineering teams.
• Eligibility to access U.S. export-controlled technology is required; employment may depend on citizenship, permanent residency, or prior U.S. license approval.

Nice to have

• Experience with SPICE, PNR, placement, CTS, routing, post-route optimization, DFT, or AI-based automation.

Culture & Benefits

• Collaborative environment focused on curiosity and solving difficult technical problems.
• Competitive compensation package and benefits.
• Equal opportunity employment.
• Opportunity to work on scalable timing models, advanced-node implementation, signoff workflows, and AI-enabled automation.