Top Artificial Intelligence Jobs in Israel

Lead development and use of advanced network simulation tools to model AI/HPC topologies and traffic. Write production C++ and Python code, drive performance modeling roadmap, validate assumptions vs hardware/software, and deliver actionable architecture insights.

The Generative AI Engineer will design AI architecture, build production features, lead prompt design, collaborate with teams, and stay updated with the GenAI ecosystem.

As a Product Manager at Port, you will define product strategy, manage cross-functional teams, and shape the developer experience for AI platforms, focusing on impact and metrics.

The AI Engineer will develop internal AI products and automation solutions, working closely with stakeholders to address business needs using data and AI technology.

Own the product lifecycle for LLM-powered recruiting features, from discovery and requirements through launch and optimization. Define product vision, roadmaps, and priorities while partnering with engineering, data science, design, and go-to-market teams. Evaluate LLM outputs, label data, analyze conversation traces, identify failure modes, and improve prompts, workflows, metrics, and system logic. Translate customer and stakeholder feedback into product decisions and guide cross-functional teams toward reliable, scalable AI experiences.

Leads IT in Tel Aviv, manages IT Solutionists, and builds autonomous employee experiences through AI-powered automations, agents, workflows, and internal tools. Partners with Product, Engineering, Operations, and Security to identify and deliver high-impact opportunities, while owning workplace technology, networks, vendors, budgets, governance, and technical support. Requires strong system design, AI, integration, prototyping, team leadership, communication, and ambiguity-management skills.

Own key parts of Torq’s agentic AI platform for security operations, including agent management, user interactions, UX, prompt engineering, and adoption. Partner with engineering to deliver capabilities, represent customer needs, monitor AI trends, and analyze product and usage data. Candidates should have 3–5 years of product management experience, strong agentic AI and LLM knowledge, at least two years in cybersecurity—ideally SecOps, incident response, threat intelligence, or threat hunting—and experience with automation products and APIs.

The Applied AI Researcher will train models, maintain benchmarks, preprocess data, conduct experiments, and collaborate with engineering teams within various AI research tracks.

The role involves conducting independent research in AI and cybersecurity, collaborating across tracks, and mentoring junior researchers. Candidates should have a strong publication record and expertise in various AI methodologies.

Lead the design and development of automated tests for UI, API, and integration layers, ensuring fast and reliable software releases. Collaborate with developers to implement robust testing strategies and integrate with CI/CD pipelines.

The role involves designing advanced AI models for cybersecurity solutions, leading research initiatives, and mentoring data scientists, focusing on LLMs and scalable systems.

The AI Software Engineer will optimize computer vision pipelines for edge systems, ensuring performance and reliability while processing real-time maritime video streams.

The AI Researcher will design, train, and deploy advanced neural network models for robotics applications, collaborating with cross-functional teams to integrate models into physical robots, focusing on machine learning lifecycle management.

Develop and maintain Agentic AI components including multi-agent orchestration and RAG workflows. Implement backend logic and data processing in Python and SQL, integrate systems via APIs, write specs and tests, support debugging and production issues, collaborate with global teams, engage end users, and document solutions.

As a Senior Software Architect, you will lead the architectural development of advanced networking applications for AI and HPC systems, collaborating with various teams to define software stacks and contribute to groundbreaking technologies.

Develop and integrate AI/ML-driven CAD solutions and automation flows for VLSI design, verification, and implementation. Research, prototype, and deploy AI algorithms; build and maintain automation scripts and infrastructure; collaborate with design and verification teams to improve workflows; evaluate AI impact on design quality and productivity; provide technical support and training for AI-enabled CAD flows.

Design and build Gong's internal agentic framework and runtime, including orchestration, evaluation, observability, safety guardrails, SDKs/APIs, and cross-language integrations to enable production-grade AI agents at scale.

The role involves conducting simulated cyber attacks, vulnerability assessments, risk analyses, and developing mitigation strategies for AI security technologies.

As a CBRN expert at Alice.io, you will evaluate and create prompts for Generative AI systems, ensuring their safety and accuracy in CBRN contexts. You'll provide expert feedback and stay updated with relevant research and regulations.

Conduct deep technical research to identify novel risks and attack vectors in cloud- and AI-native environments, produce proofs-of-risk and technical POCs, and collaborate with Product and Engineering to translate research into product capabilities.

Define and drive AIOps and AI-data-readiness strategy and roadmap; lead customer discovery with enterprise accounts; oversee deployment models for cost, governance, privacy, and compliance; take products from PRD to GA with R&D/QA/Support; measure adoption and business outcomes; act as AIOps subject-matter expert.

Lead and scale AI red-teaming and security research teams to design and execute adversarial attacks against LLMs, generative and agentic AI, build automated red-teaming engines, evaluate adversarial behaviors, and partner with product and engineering to implement guardrails, monitoring, and secure deployment practices.

Build and extend a production LLM agent platform: design agents and tools, implement integrations, own RAG ingestion and retrieval, define integration contracts, drive evaluation-led development, operate deployments and observability, and establish AI-specific engineering practices across frontend and backend.

Lead the company's internal AI strategy, drive AI adoption across departments, build agent platforms and machine-readable knowledge repositories, optimize processes with AI workflows, establish AI governance, security, and FinOps, and enable business units to use AI-assisted development.

Build and own agentic workflows and AI platform capabilities that accelerate the R&D software development lifecycle. Discover bottlenecks, design and ship production-grade LLM/agent systems, create evaluations and safety/feedback loops, integrate platform infrastructure, and prove KPI-driven impact while partnering with engineering teams.