Top Artificial Intelligence Jobs in Israel

Design and architect storage systems for AI clusters (training, inference, KV cache, RAG). Research workloads, lead hands-on POCs, define APIs/protocols, and drive hardware features for DPUs/NICs while collaborating with hardware architects, software teams, and external customers.

Lead development of AI-driven verification tooling: build AI ecosystems, integrate ML/LLM agents into verification flows, automate stimulus generation and coverage closure, create intelligent debug assistants, research AI approaches to state-space explosion, and train teams on human-in-the-loop methodologies.

The role involves developing techniques to GPU accelerate AI workloads, optimizing complex AI algorithms, and collaborating with experts to improve hardware and software design.

The AI Developer role involves designing and integrating AI agents, developing full-stack features, maintaining backend services, and continuous experimentation.

Define product strategy and roadmaps for custom AI accelerator platforms and server systems. Translate customer and engineering needs into PRDs and technical specifications, manage schedules and cross-team dependencies, guide product launches, and align stakeholders across silicon, systems, software, and operations.

Lead the AI software stack and compiler platform: define PRDs and roadmaps for compiler toolchains, runtime engines, framework integrations, kernel optimizations, and scale-out orchestration. Coordinate cross-team schedules, partner with framework vendors, and champion developer experience to enable seamless execution on Majestic's Ignite APU.

Lead and deliver AI-driven engineering initiatives from scoping and architecture through hands-on execution. Drive adoption of AI tooling and practices across Dev and DevOps, identify opportunities to improve velocity, incident response, cost efficiency and reliability, align stakeholders, unblock dependencies, and evaluate emerging AI tools for production-grade solutions.

Lead AI-driven creative and marketing design: own team efficiency, standardize generative-AI workflows (Weavy), set creative direction, manage the marketing design board, build reusable component libraries and self-serve asset systems, partner with Marketing, and track performance and user testing to drive growth.

Own and evolve a large-scale GCP platform (GKE, networking, IAM, GitOps, IaC) and design a production agentic AI ecosystem. Responsibilities include reliability, observability, FinOps, agent orchestration, vector DB/RAG patterns, AI evaluation and guardrails, cost/usage monitoring, and defining reference architectures and standards for teams building LLM/agent features.

Design and build internal AI platforms, SDKs, and MCP servers; embed agentic LLM capabilities into developer platforms and CI/CD; develop single- and multi-agent workflows; implement AI observability, evaluations, security guardrails, and tooling; own platform architecture to accelerate adoption across engineering teams.

Define end-to-end NVL system architecture for next-generation high-performance and AI clusters. Research solutions across algorithms, software, firmware, and hardware; build simulation and performance models; analyze results; and collaborate with cross-functional teams to drive product lifecycle from pre/post-silicon to deployment.

Design and build an AI-native, production-grade full-stack system from scratch. Own features end-to-end, integrate LLMs, RAG pipelines, agents and ML models into production, ensure performance, reliability, guardrails and observability, and improve team platforms, patterns, and tooling.

Lead identification, design, and implementation of scalable generative-AI solutions. Evaluate models and tools, build prompts, RAG pipelines and AI agents, integrate workflows with systems, run projects from ideation to rollout, and guide organizational AI adoption.

Develop, train, evaluate, and deploy detection models for AI security. Translate threat research into production-ready models across ML, deep learning, LLMs, and agentic systems. Build benchmarks, labeled datasets, monitor production performance, and iterate with security researchers to ensure reliability and measurable impact.

The Senior Software Engineer will develop AI-focused software systems, build automation tools for data centers, and collaborate on system performance improvements.

Lead end-to-end QA for cyber-focused features: define and execute test plans (functional, integration, API, regression, exploratory), analyze logs, investigate defects, validate automation coverage, collaborate with developers and product, use AI tools to enhance testing, and contribute to QA documentation and release readiness.

Build AI-driven internal tools, automations, and workflows for Sales and Marketing. Own GTM tech stack, analyze revenue funnel, create dashboards, playbooks, AI agents, and integrate systems to improve conversion and efficiency.

Lead and build a quality engineering organization that owns automation, performance, and AI-driven quality. Define AI quality strategy, deploy LLM-powered test generation, architect scalable test frameworks, embed shift-left testing in CI/CD, run enterprise-scale performance validation, set KPIs/SLAs, and hire and mentor engineering and architect-level talent while partnering with Product and SRE.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Lead and build a Quality function for an AI-native R&D org: own in-Machine quality gates, production verification, monitoring-as-code, evals of LLM/agent output, set quality metrics, and manage a ~9-person team to ensure reliability and reduced regressions.

Develop AI models and algorithms for new products, analyze data, collaborate with engineers, and keep updated with AI trends.

The role involves evaluating AI outputs, generating original content, reviewing texts, and ensuring cultural relevance. Candidates should have native fluency in Hebrew and a strong command of English, with attention to detail and the ability to follow guidelines.

Lead a team to develop AI-driven products, manage engineering processes, analyze product data, and improve AI performance and outcomes.

Lead backend development and production deployment of LLM-based hiring agents: research LLMs, implement model-driven features, build evaluation frameworks, design scalable architecture, adopt CI/CD and cloud best practices, and mentor engineers.

Review, evaluate, and annotate AI-generated multilingual and multimodal content (text, images, audio, video). Perform quality checks, identify edge cases, contribute to dataset development, participate in calibration, and provide feedback to improve annotation guidelines and workflows.