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we are establishing a strategic R&D center in Israel to drive the development of complex semiconductor chips that solve the critical 'data bottlenecks' enabling the future of AI at scale. As we expand our presence in Israel, we're seeking a highly skilled Physical Design CAD Engineer specializing in CAD Extraction to join our local engineering powerhouse from the ground up.
This is a unique opportunity to take on meaningful technical ownership in a new site, implementing the parasitic extraction (PEX) methodologies and flows for chips that power the world's largest AI clusters. As a foundational member of the team, you will be responsible for the accuracy and efficiency of our extraction environment, ensuring that our high-speed designs are modeled with the highest precision from RTL to GDSII.
Key Responsibilities
Develop, qualify, and maintain automated RC extraction flows for high-performance AI SoCs
Own the setup and validation of foundry technology files (e.g., StarRC/Quantus techfiles, TLU+, ITF) across various process corners
Perform correlation studies between different extraction tools and 3D field solvers (e.g., Raphael, QuickCap) to ensure modeling accuracy
Collaborate closely with the Signal Integrity (SI) and Power Integrity (PI) teams to provide accurate parasitic data for critical high-speed nets and power grids
Implement automated scripts (Tcl/Python) to streamline extraction regressions, data parsing, and PEX-to-STA (Static Timing Analysis) handoffs
Analyze the impact of layout effects (LDE) and parasitics on timing and power, providing feedback to the implementation team to optimize PPA
Interface with EDA vendors and foundries to resolve extraction tool bugs and methodology gaps related to advanced nodes (5nm/3nm).

we are establishing a strategic R&D center in Israel to drive the development of complex semiconductor chips that solve the critical 'data bottlenecks' enabling the future of AI at scale. As we expand our presence in Israel, we're seeking a highly skilled Physical Design Engineer specializing in EMIR CAD to join our local engineering powerhouse from the ground up.
This is a unique opportunity to take on meaningful technical ownership in a new site, executing the backend power methodologies for chips that power the world's largest AI clusters. As a Physical Design Engineer, you will be a core technical contributor ensuring the power robustness and long-term reliability of our high-performance connectivity silicon.
You will continuously develop the Electro-Migration and IR Drop (EMIR) flow, working closely at the intersection of Physical Design, Analog/Mixed-Signal design, and Package Engineering.
Key Responsibilities
Take responsibility on IR drop analysis and signal/power electromigration (EM) flow
Implement and maintain robust EMIR flows and methodologies using industry-standard tools (Ansys RedHawk-SC, Cadence Voltus, or equivalent)
Collaborate closely with Analog/SerDes designers to integrate current profiles and ensure robust power delivery to sensitive high-speed IP blocks
Partner with Package Design engineers to perform Chip-Package-System (CPS) co-analysis flow
Understand root-cause analysis for voltage drop violations and EM risks
Support silicon bring-up by correlating simulation results with actual silicon measurements and yield data.

we are establishing a strategic R&D center in Israel to drive the development of complex semiconductor chips that solve the critical 'data bottlenecks' enabling the future of AI at scale. As we expand our presence in Israel, we're seeking a highly skilled Static Timing Analysis (STA) Engineer to join our local engineering powerhouse from the ground up.
This is a unique opportunity to take on meaningful technical ownership in a new site, executing the sign-off methodology for chips that power the world's most advanced AI clusters. As an STA Engineer, you will be deeply involved in the STA activities from chip partition and time budgeting through to final sign-off. You will bridge the gap between Architecture, Design, DFT, and Physical Design to ensure our high-performance silicon meets the aggressive timing targets required for next-generation connectivity.
Key Responsibilities
Execute the STA flow and sign-off methodologies, ensuring our products meet rigorous timing criteria for the most demanding data center environments
Collaborate closely with Architecture, Design, DFT, and Backend teams, participating in timing reviews and working with block owners to navigate the path to sign-off convergence
Develop, optimize, and manage complex SDC constraints, ensuring they are accurate and robust across multi-scenario environments
Analyze and resolve challenges related to cross-chip clock distribution networks and apply sophisticated margining techniques to ensure robust silicon across all process corners
Participate in design methodology improvements and tool automation, utilizing both industry-standard EDA tools and custom scripts to make our sign-off process faster and more efficient.

We are looking for a highly analytical, detail-oriented Innovation Expert to join our Open Innovation team. This role is ideal for an individual contributor operating at the intersection of deep tech, the startup ecosystem, and academia, with strong technology analytical skills, clear communication, and a foundational background in computer architecture research.
In this role, you will drive our external innovation strategy by managing academic research collaborations, scouting early-stage deep-tech startups, and conducting technical due diligence.
Key Responsibilities:
Academic Cooperation & Research Partnerships: Identify and build direct collaboration agreements with world-class professors, research labs, and universities. Facilitate joint research projects, tech-transfer initiatives, and track academic spin-offs.
Startup Scouting & Ecosystem Mapping: Actively identify, source, and map early-stage deep-tech startups and emerging tech companies globally within the companys strategic domains.
Technical Due Diligence: Evaluate startup technologies, product-market fit, and team capabilities. Analyze technical validity and assess how external innovations align with our internal roadmaps.
Ecosystem Analysis: Conduct deep-dive analyses of startup ecosystems, venture capital trends, and academic spin-offs to identify high-potential investment and collaboration opportunities.
Investment & Collaboration Support: Synthesize technical and commercial findings into clear, structured scouting reports, investment tech memos, and presentations for senior leadership to drive partnership or investment decisions.
Cross-Border Collaboration: Act as a bridge between startup founders, academic innovators, and internal global teams (including European and China-based business units) to facilitate joint POCs, or investment reviews.
Track Tech Domains: AI infrastructure, advanced semiconductors, hardware-software co-design, and AI data movement.

we are establishing a strategic R&D center in Israel to drive the development of complex semiconductor chips that solve the critical 'data bottlenecks' enabling the future of AI at scale. As we expand our presence in Israel, we're seeking a visionary Staff DFT Engineer to help build our local engineering powerhouse from the ground up. This is a unique opportunity to take on meaningful product ownership in a new site, ensuring the reliability and testability of chips that power the world's largest AI clusters.
As a Staff DFT Engineer at our company, you will be at the intersection of architecture, design, and production. You won't just run tools-you will be a foundational member of the team responsible for the entire lifecycle of our silicon's reliability. From defining initial DFT architecture to supporting post-silicon bring-up, your work ensures that the backbone of AI infrastructure connectivity is flawless and scalable. If you thrive on solving complex challenges in deep-submicron processes and want to establish world-class DFT methodologies, this is your opportunity.
Key Responsibilities
DFT Architecture & Strategy
Own the DFT journey from high-level architecture definition and RTL design to backend implementation and post-production support
Develop comprehensive Design-for-Testability (DFT) strategies for next-generation connectivity platforms, ensuring chips meet the highest quality standards
​DFT architectures including JTAG/iJTAG, MBIST, Scan, and ATPG methodologies
Test Pattern Development & Optimization
Generate and optimize high-quality test and debug patterns for production
Perform Static Timing Analysis (STA) for DFT modes and conduct gate-level simulations to ensure robust performance
Drive test coverage and quality metrics to meet stringent manufacturing requirements
Cross-Functional Collaboration & Methodology Innovation
Act as a multidisciplinary bridge, collaborating closely with Architecture, Verification, and Backend teams to ensure seamless integration and optimal QoR
Participate in developing and maintaining cutting-edge DFT implementation flows
Automate and improve methodologies using advanced scripting and tools.

we are looking for a Wafer-Scale System: Computation (LLM) and Communication (NoC) Co-Design.
Responsibilities:
Conduct cutting-edge research on the co-design of communication and computation for wafer-scale systems. On the communication side, the work focuses on architecting Networks-on-Chip, providing scalable, high-bandwidth, and ultra-low-latency interconnects that efficiently span multiple dies and support fine-grained data movement. On the computation side, the research emphasizes optimizing and mapping training and inference workloads to fully exploit wafer-scale resources, improving performance, scalability, and energy efficiency through intelligent task placement and scheduling.
Investigate various aspects of system design, including:
Network-on-Chip design, further including Topology exploration, routing algorithms, and protocol/flow-control design to support high-throughput collective communication, low-latency synchronization, and efficient model/data parallelism
Expressing and Mapping LLM Parallelism to Wafer-Scale System that simplify LLM parallelism (e.g., tensor, pipeline, and data parallelism) and efficiently map training and inference workloads onto wafer-scale system