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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 talented Senior Emulation Engineer to help build our local engineering powerhouse from the ground up. This is an exciting opportunity to take on meaningful ownership in a new site, implementing the emulation strategy for chips that power the world's largest AI clusters.
As an Senior Emulation Engineer, you will be a core technical driver of our Israel R&D center, working at the intersection of hardware and software to ensure our silicon meets extreme quality and performance targets. You will execute end-to-end emulation flows, bridge the gap between RTL and functional validation, and partner with cross-functional teams to enable seamless hardware-software integration. If you thrive on solving complex technical challenges and want to play a key role in validating cutting-edge AI infrastructure connectivity solutions, this is your opportunity.
Key Responsibilities
Emulation Flow Execution & Implementation
Execute end-to-end emulation flow from high-level model generation and RTL synthesis to complex system-level testing and silicon-accurate debugging
Work directly with next-generation emulation platforms (Zebu, Palladium, or Veloce) to implement cutting-edge methodologies
Maintain and evolve emulation flows to reduce compile times and increase execution speed, directly impacting time-to-market
System-Level Debug & Validation
Drive initial model bring-up process in high-stakes environment, identifying and resolving complex bugs
Ensure rapid cycles from RTL to functional stability through systematic debug approaches
Own technical blocks and drive them to completion independently
Cross-Functional Collaboration
Partner with Firmware, Software, and Validation teams to debug complex system-level scenarios
Ensure seamless hardware-software integration for AI infrastructure connectivity
Collaborate with Design and Verification teams to optimize emulation strategies.

We are looking for an outstanding Research Team Lead with deep hands-on expertise in large-scale distributed systems, AI framework infrastructure, and performance optimization on custom accelerators. If you are a visionary technical leader, a skilled communicator, and a team builder who thrives at the frontier of systems and AI research - you're welcome on board.
What Will You Be Doing?
Lead and grow a world-class team of researchers and engineers working on distributed AI infrastructure and systems software
Architect and own the software infrastructure enabling distributed training and inference on Huawei's custom accelerator hardware (e.g., Ascend NPU)
Drive research and development on communication libraries, runtime systems, memory management, and graph execution & synchronization at scale
Optimize end-to-end performance across large-scale clusters, covering both scale-up (multi-device) and scale-out (multi-node) configurations
Design and implement high-performance communication backends and collective operations (AllReduce, AllGather, broadcast) for distributed training workloads
Collaborate cross-functionally with hardware architects, compiler teams, and framework engineers to co-design hardware-software solutions
Publish and present research findings at top international venues (NeurIPS, EuroSys, SC, MLSys, OSDI) and represent the team externally
Mentor engineers and researchers, conduct performance and growth reviews, and shape team culture and technical direction
Partner with top academic institutions and open-source communities to advance the state of the art in distributed AI systems

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 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 an experienced and strategic IT Manager to lead Earnix's global IT function. Reporting to senior leadership, you will own and evolve the IT infrastructure, service delivery, security posture, and AI integration capabilities that underpin the business across our worldwide offices. You will manage a globally distributed team covering help desk support, systems administration, information security, and AI-driven tooling — ensuring reliable, secure, and scalable IT services for a fast-growing SaaS company.
?What You Will Do Own and continuously improve global IT operations, including help desk support, systems administration, endpoint management, and IT infrastructure across all Earnix offices and remote users. Partner with business and technology stakeholders to translate operational and growth needs into scalable IT solutions, policies, and strategic roadmaps. Lead the implementation and ongoing management of the company’s information security program, ensuring compliance with SOC 2, ISO 27001, and relevant regulatory frameworks globally. Drive the adoption and integration of AI-powered tools and automation across IT workflows, enabling global teams to work more efficiently and securely. Build, manage, and develop a high-performing, globally distributed IT team, setting clear goals, driving best practices, and fostering a culture of service excellence.

Position Intro:
Earnix is the premier provider of mission-critical, cloud-based intelligent decisioning across pricing, rating, underwriting, and product personalization. These fully integrated solutions provide ultra-fast ROI and are designed to transform how global insurers and banks are run by unlocking value across all facets of the business. Earnix has been innovating for insurers and banks since 2001, with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.

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

Join a team developing next-generation AI accelerators, working at the intersection of AI algorithms, software, and hardware.