
AI processors and RISC-V computing
Physical Design Engineer: Die-to-Die Interface (RTL to GDSII)
필수 스킬
Python
Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities.
Tenstorrent is seeking a highly skilled Physical Design Engineer to drive the critical Die-to-Die (D2D) Physical Implementation from RTL to GDSII. This role demands deep expertise in full physical design flow with a specific focus on closing high-speed D2D interfaces for multi-die/chiplet architectures.
This role is remote role open to any location in the U.S.
We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.
Who You Are
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A seasoned ASIC Physical Design Engineer with 5+ years at advanced nodes (7nm or below) and multiple successful tapeouts.
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Strong in full-chip implementation, comfortable owning blocks from RTL to GDSII across synthesis, floorplanning, place-and-route, CTS, and sign-off.
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Deeply familiar with high-speed interfaces (D2D, PCIe, HBM, Ser Des) and the physical challenges that come with them (timing, signal integrity, power integrity).
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Detail-oriented and methodical with STA, constraints, and closure for complex, high-speed designs.
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A hands-on problem solver who enjoys collaborating across analog, digital, and full-chip teams to debug tough issues.
What We Need
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Lead Die-to-Die (D2D) physical implementation and closure, taking high-speed D2D PHYs/controllers from netlist to tapeout.
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Own the full PD flow (RTL-to-GDSII): synthesis, floorplanning, P&R, CTS, optimization, and sign-off.
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Drive timing and verification, including full STA (setup/hold), SI analysis (crosstalk, IR drop), and achieving sign-off quality DRC/LVS.
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Improve and maintain physical design methodologies, flows, and automation scripts (Tcl, Python), with specific focus on D2D routing, power grid design, and timing closure.
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Partner closely with full-chip/chiplet teams to meet all tapeout requirements and with the analog design team to resolve interface issues (LEF/LIB, constraints, integration/debug).
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Apply strong EDA tool expertise across Synopsys/Cadence/Mentor for implementation, analysis, and verification.
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Bring a solid academic foundation: B.S./M.S. in EE/CE or related field.
What You’ll Learn
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How to implement and close cutting-edge D2D interfaces in advanced multi-die/chiplet architectures.
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Advanced strategies for co-optimizing routing, power grid, and timing for high-speed links across dies.
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Deeper integration techniques working with analog PHY teams, including model handoff (LEF/LIB) and constraint-driven interface design.
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How to drive repeatable, scalable PD methodologies for high-speed interfaces that can be leveraged across future chiplets and products.
Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made.
Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.
*This position requires access to technology that requires a U.S. export license for persons whose most recent country of citizenship or permanent residence is a U.S. EAR Country Groups D:1, E1, or E2 country. *
This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
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Tenstorrent 소개

Tenstorrent
Series CTenstorrent is a semiconductor company that develops AI accelerator chips and software for machine learning workloads. The company focuses on creating scalable processor architectures for data centers and edge computing applications.
201-500
직원 수
Toronto
본사 위치
$2.6B
기업 가치
리뷰
10개 리뷰
3.8
10개 리뷰
워라밸
3.2
보상
2.8
문화
4.1
커리어
3.4
경영진
4.2
72%
지인 추천률
장점
Supportive management and strong leadership
Great team culture and fantastic colleagues
Cutting-edge technology and challenging projects
단점
Heavy workload and frequent overtime
Fast-paced and stressful environment
Below industry standard salary
연봉 정보
24개 데이터
Director
Director · DIRECTOR, DESIGN FOR TEST ENGINEERING
1개 리포트
$382,588
총 연봉
기본급
$294,299
주식
-
보너스
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$382,588
$382,588
최근 소식
Tenstorrent Previews Large Compute Cluster, Generates Video Faster Than Real Time - EE Times
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Ex-Tenstorrent Execs Start Cloud Provider, AI Lab in Japan - EE Times
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6w ago
Interview with Toloka CEO (in Russian)
If you happen to understand Russian, here is a 2h interview with Toloka CEO Olga Megorskaya. Few things that I noted: * Industry is called Human Data, the biggest competitor is Scale AI. Basically they know how to produce human-generated data of a high quality that is used to train/post-train AI. These days it's a highly skilled people, sometimes with PhDs or many years of experience, but humans are unreliable, don't follow instructions, etc. - so it is a challenge to produce high quality data
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