Machine Learning Engineer

An elite Stealth-Mode startup backed by premier global VCs, dedicated to revolutionizing the Cloud Security landscape.

Founded by industry veterans with a proven track record of scaling category-defining security products, the company is architecting an AI-native SaaS platform designed to solve the most complex infrastructure protection challenges.

Located in a prime Tel Aviv hub (Midtown/Train station vicinity), the company fosters a high-impact engineering culture with a flexible hybrid model (2 days WFH).

Responsibilities-

• End-to-End AI Ownership: Leading the vision and execution of AI-driven capabilities—from initial concept and experimentation to full production deployment.
• Agentic Frameworks: Designing and building AI Agents that assist developers in securing cloud environments, optimizing workflows, and accelerating remediation.
• Advanced LLM Orchestration: Implementing complex RAG pipelines, Agent Orchestration, and Guardrail systems to ensure secure and reliable AI interactions.
• Cloud Security Intelligence: Developing AI features for Policy Generation, Drift Detection, and automated security response within Multi-Cloud environments (AWS, OCI, etc.).
• Applied AI Excellence: Establishing the infrastructure for Prompt Iteration, Evaluation frameworks, and Feedback Loops to ensure the continuous improvement of the product’s AI models.

Requirements-

• Academic Foundation: M.Sc. in Computer Science, Mathematics, Physics, Statistics, or a related quantitative field – Mandatory.
• Professional Seniority: At least 5 years of experience as a Data Scientist or AI Engineer in a product-oriented environment – Mandatory.
• ML & Generative AI Mastery: Deep, hands-on experience in Machine Learning and significant exposure to LLMs and Generative AI systems – Mandatory.
• Production Orientation: Proven track record of deploying AI/ML models into Production environments (preferably SaaS).
• Cloud Native: Familiarity with Cloud architectures (AWS/GCP/Azure) and the security challenges inherent in modern infrastructures.