Top Artificial Intelligence Jobs in Israel

Coordinate complex, cross-team AI Engineering initiatives; translate strategies and roadmaps into execution plans; drive adoption of ML development workflows; define success metrics; engage users and partners; manage priorities, risks, blockers, communication, and group planning to deliver measurable outcomes.

Own the strategy, roadmap, user experience, and outcomes for Similarweb’s AI-native B2B SaaS platform. Lead an engineering team alongside design, define AI agent capabilities and quality standards, coordinate with AI, data, and platform teams, conduct customer discovery, analyze product performance, and partner with go-to-market teams to launch and grow the product.

Leads a team of front-end VLSI engineers developing AI processors, DSPs, and hardware accelerators. Owns architecture, design, validation, release, PPAA targets, design methodologies, signoff, and AI tool adoption. Collaborates with architecture, software, hardware simulator, CAD, and backend teams, while supporting system bring-up, debugging, and customer activities.

Lead an embedded software engineering team responsible for designing and delivering Ceva’s NPU AI software stack, including host SDKs, runtimes, APIs, drivers, and embedded firmware. Provide technical direction, mentorship, cross-functional collaboration, roadmap alignment, and delivery oversight while developing high-performance connectivity between NPU hardware and host CPUs.

Lead a rapid-execution engineering pod developing production-grade AI agents and LLM applications for Skai’s Agents Studio platform. Translate customer feedback and market needs into scalable solutions, prototypes, backlogs, and platform improvements. Establish GenAI-native development workflows, build context layers and guardrails, and balance rapid delivery with enterprise-grade reliability, security, and maintainability. Mentor engineers and collaborate closely with product teams, forward-deployed engineers, and clients.

Leads LiveU’s enterprise Data, Analytics, and AI roadmap by building a scalable cloud data platform, deploying production AI and ML solutions, enabling company-wide GenAI adoption, and delivering self-service BI. Oversees data governance, security, compliance, FinOps, systems integration, and executive reporting while managing and mentoring data and AI engineers and analysts.

Build and operate production-grade multi-agent AI systems for network and AI infrastructure automation. Responsibilities include developing backend services and APIs, deploying systems on Kubernetes, implementing RAG and retrieval pipelines, integrating LLMs and tools, and building tracing, evaluation, guardrails, observability, and safety capabilities. The role requires strong distributed systems, debugging, database, and production operations expertise.

Leads FP&A for the global Academia & Government segment, overseeing P&L reporting, forecasting, planning, variance analysis, revenue and customer metrics, financial modeling, and executive decision support. Partners across Sales, Operations, Data, HR, Accounting, and other functions to improve reporting and performance management. Prepares executive and board materials, supports strategic investments and transformation initiatives, and leads, coaches, and develops the FP&A team.

Leads Stampli’s AI-native engineering transformation by building teams, coding-agent capabilities, agentic development workflows, AI-ready CI/CD requirements, isolated environments, governance, and measurable adoption programs. Owns initiatives from experimentation through organizational scale, reshaping the software development lifecycle and team structures. Partners with R&D, DevOps, and Security to establish responsible, scalable AI development practices and drive technology vision, innovation, productivity, and business impact.

Drive development of AI and cybersecurity products from concept to launch. Own roadmap development, collaborate with engineering, research, design, R&D, and CTO teams, engage customers and sales, monitor cybersecurity trends, and deliver scalable cloud security solutions. Serve as a technical subject matter expert while partnering with Product Marketing and Customer Success to create strong customer outcomes.

Build and productionize LLM-based agents that investigate, triage, and remediate cloud security threats. Train and fine-tune models, develop AI platform services and evaluation pipelines, conduct rigorous research, and collaborate with security, product, and engineering teams to deliver autonomous security workflows.

Own end-to-end execution of the Sovereign AI roadmap across Research, Engineering, Product, and Design. Manage concurrent technical workstreams, dependencies, risks, launches, stakeholder alignment, and prioritization. Build scalable programs from 0 to 1, unblock teams, improve operational processes, and support commercial, billing, pricing, and developer platform operations. Translate technical and execution realities into clear decisions for leadership and government stakeholders.

Design and build scalable backend systems and production-grade AI solutions for cyber-physical security environments. Responsibilities include owning features from architecture through deployment, developing LLM applications, RAG systems, and AI agents, integrating AI-assisted engineering tools, and promoting resilient, clean software architecture across cloud and on-premise environments. The role requires strong Python backend development, distributed systems expertise, real-time data and networking experience, and hands-on AI application deployment.

Creates and evaluates adversarial attacks against LLMs and AI systems, including prompt injection, jailbreaks, agent manipulation, and tool-use attacks. Develops scalable attack-generation systems, benchmarks, datasets, and regression tests; assesses attack quality and model vulnerabilities; and communicates findings to data science, security, and engineering teams to improve AI robustness.

Owns the consolidation, prioritization, and definition of cross-product clinical AI requirements. Partners with Product Managers and AI, data, clinical, UX, and R&D teams to create unified feature specifications, user flows, and quality requirements. Maintains requirements documentation, resolves conflicts, identifies risks, and ensures traceability for scalable, system-agnostic clinical AI capabilities and regulatory processes.

Leads Cynet’s Product, Engineering, and Counter Adversary Operations organizations, owning company-wide technology and product strategy, AI adoption, product quality, release predictability, cybersecurity platform integration, and customer outcomes. Serves as Managing Director of the Tel Aviv office, overseeing site stability, culture, talent attraction, employee experience, and cross-team leadership. Partners with executive leadership to align technology and product strategy with commercial goals.

Build and own AI agent-powered content creation features across the stack using Node.js, TypeScript, and React. Develop agents that process audio, video, and text to create and edit professional content, while shaping user experiences for reviewing and applying agent outputs. Collaborate closely with Product, manage dependencies, work through ambiguity, and use AI tools for development and debugging.

Build and deploy practical AI solutions, including AI agents, agentic workflows, LLM integrations, and business automations. Partner with cross-functional teams to identify opportunities, evaluate AI platforms, architect enterprise integrations, optimize prompts, and lead initiatives from prototyping through production. Ensure solutions are scalable, secure, governed, and aligned with business outcomes while communicating effectively with technical and business stakeholders.

Conduct applied AI and machine learning research for exam integrity, behavioral analysis, educational assessment, and analytics. Develop explainable models that fuse multimodal data, investigate noisy real-world datasets, design and evaluate experiments, and translate findings into production features. Collaborate directly with the CTO and engineering team while balancing academic research with software development and product delivery.

Serve as the founding customer-facing engineer for Kimchi, leading product demonstrations, customer workspace setup, inference API integrations, model routing optimization, and governance policy implementation. Configure agent workflows, integrations, budgets, cost attribution, and access controls. Translate customer feedback into product improvements while identifying opportunities for revenue expansion. The role combines hands-on LLM and inference expertise with customer enablement and commercial thinking in a remote-first environment.

Own the roadmap for shared AI platform capabilities, conduct customer discovery, translate market needs into product requirements, and partner with engineering and research teams on delivery. Build AI-enabled workflows, evaluation processes, and quality gates while supporting demos, proposals, positioning, and commercial conversations. Ensure products meet sovereign-environment requirements including on-premise deployment, data residency, access control, auditability, and resilience.

Own the renter growth strategy, product roadmap, and end-to-end conversion funnel. Drive qualification improvements with Data Science and ML models, run rigorous UX and AI experimentation, optimize embedded experiences across property management systems, develop adoption incentives, and manage partnerships. The role requires accountability for growth metrics, strong fintech and payments expertise, analytical self-service, UX judgment, and the ability to execute in an ambiguous environment.

Own strategic post-sales relationships with enterprise customers, advising on AI security and governance programs. Lead discovery, define measurable outcomes, drive platform adoption, support retention and expansion, and coordinate Product, Engineering, Security, Support, and operationalization teams. Translate CISO-level strategy, GRC requirements, business objectives, and technical needs into practical solutions for securing AI applications, LLMs, RAG systems, agents, and development pipelines.

Build and operate production-grade AI agents in Python for customer-facing applications. Design multi-step workflows involving intent understanding, context, memory, tools, APIs, business logic, and external systems. Own backend services end to end, including architecture, deployment, reliability, observability, evaluation, regression testing, and continuous improvement. Collaborate with product and engineering teams to deliver scalable, safe, and reliable agentic experiences.

Develops internal AI-powered products and tools, evaluates and integrates AI development solutions, builds proof-of-concept projects, establishes prompt engineering practices, creates training materials, and advises leadership on AI adoption across engineering teams.