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we are looking for a payment specialist to be part of the Finance team in Israel (the company is located in both Hod Hasharon & Haifa, the position is in Hod Hasharon)
As key positions for treasury security department, perform high quality payments and collections, ensure compliance with companys policies and procedures.
Processing payments and funds transfer on different e-banking platform, and ensure there are no errors by conducting the necessary checks;
Manage issues in the payment process, such as bank system problems, payment rejections, refunds, etc. Verify the root causes and resolve them to ensure timely payments.
Assist in the providing of bank statements, identify issues in bank statements and resolve them;
Establishment payment rule book guidance, consider the optimization points in the work, to make daily operations more standardized and control payment risk;
Support and perform necessary cooperation for APs department month closing;
Managing the Expense reports reimbursement for all employees
Assist with other finance tasks as needed, such as managing financial operations, assisting FP&A with preparing budget reports etc.,Qualifications and Professional requirements

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 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