Top Machine Learning Jobs in Israel

Lead innovation for Riskified’s Identity Graph Engine by researching, architecting, and deploying large-scale machine learning models for identity resolution and fraud prevention. Build models across graph, tabular, and sequence data; guide research roadmaps and technical standards; integrate ML pipelines into real-time production systems; benchmark academic research; collaborate with engineering and product teams; and mentor senior data scientists.

Leads advanced data science and machine learning initiatives across ITOM products, from ideation and experimentation through production deployment. Develops models for anomaly detection, noise reduction, root cause analysis, NLP, and AI-powered features. Partners with Product, Engineering, Quality, and Architecture teams, defines evaluation methodologies, drives technical direction, mentors data scientists, and applies emerging AI technologies including LLMs and generative AI.

Lead end-to-end data science and machine learning initiatives across ServiceNow ITOM products, from ideation and experimentation through production deployment. Develop advanced models for anomaly detection, noise reduction, root cause analysis, NLP, and AI-powered features. Partner with Product, Engineering, Quality, and Architecture teams, define evaluation metrics, drive technical direction, and mentor data scientists while applying emerging AI, LLM, and generative AI techniques.

Own end-to-end AI and machine learning initiatives for cybersecurity vulnerability management, from research and prototyping through scalable production deployment and monitoring. Build Python-based ML pipelines on AWS and Spark, integrate LLM and generative AI capabilities, and develop solutions for automated detection, prioritization, and remediation. Collaborate with researchers, data engineers, and product leaders to address complex cybersecurity problems.

The role involves advancing LTX-Video models, designing machine learning models, conducting experiments in generative media, and collaborating across teams to implement impactful features.

Lead and grow a distributed engineering organization building customer-facing security capabilities: proprietary scanners, AI/ML detection engines, agentic remediation, vulnerability management, and security foundations. Set vision and roadmap, drive architecture and delivery, partner with product management, establish engineering standards, and represent the group in cross-functional and customer forums.

Leads architecture and hands-on C++ implementation for AI-powered embedded driver and cabin monitoring systems. Owns technical decisions, software quality, performance optimization, and integration with hardware accelerators in real-time Linux environments. Partners with architects, algorithm, systems, and product teams; mentors engineers and promotes engineering excellence. The role focuses on computer vision, machine learning, scalable embedded software, and safety-aware automotive development.

Design, train, and deploy large-scale machine learning models for bid optimization, price and win-rate prediction, CTR/CVR estimation, and auction optimization. Own the full ML lifecycle—research, experimentation, production deployment, A/B testing, monitoring, and optimization—while partnering with Product, Engineering, and Business teams and mentoring junior scientists.

Develop international client opportunities, negotiate campaign terms and budgets, build a sales pipeline, and manage deals from prospecting through campaign launch. Coordinate with Media, Operations, Legal, and Customer Success teams while developing long-term partnerships and expanding client budgets. The role requires sales or business development experience, strong relationship-building and negotiation skills, target orientation, adaptability, and an interest in AdTech or mobile performance marketing.

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.

Designs, builds, and evaluates research prototypes and production-ready systems for portable AI agents, model routing, provider interoperability, memory, retrieval, tool use, and multi-agent workflows. Responsibilities include developing evaluation frameworks, conducting statistically rigorous experiments, creating schemas and APIs, optimizing model and agent systems, and translating research into secure, reliable, reusable components. The role collaborates across research, infrastructure, security, product, and engineering teams and communicates findings through technical reports, benchmarks, demonstrations, open-source releases, and publications.

Develop and operate full-stack cybersecurity solutions across Microsoft Security teams. Responsibilities include building backend cloud or on-premises services, developing scalable front-end applications, improving service lifecycles, analyzing complex systems, streamlining deployments, and collaborating across engineering, security, data science, and program teams. The role also involves using AI-assisted tools and integrating AI capabilities into resilient, large-scale systems.

Leads the global Data Science function, setting strategy and managing teams while driving machine learning, optimization, forecasting, and statistical modeling solutions. Partners with Product, Engineering, Customer Success, and executives to translate business challenges into scalable analytical products, establish experimentation and model evaluation practices, support customer engagements, and shape the company’s AI roadmap.

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.

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 the end-to-end architecture and technical direction of production AI/ML systems for cellular RAN networks. Lead model selection, agent orchestration, RAG, data pipelines, evaluation, deployment, monitoring, and lifecycle management. Prototype critical components, establish engineering standards, mentor engineers, and collaborate with RAN, PHY, software, product, and customer-facing teams. Apply LLMs, classical machine learning, anomaly detection, and time-series analysis to network operations and wireless challenges.

Develops and integrates machine learning and deep neural network solutions into real-time Radio Access Network products. Responsibilities include creating proof-of-concepts, demonstrating improvements over industry algorithms, integrating algorithms into core technology, and collaborating with algorithms and engineering teams. The role requires production ML experience, complex systems expertise, data science proficiency, and strong communication skills in Hebrew and English.

Develop registration, scoring, image-processing, defect-detection, and localization algorithms for semiconductor metrology and inspection systems. Build zero- to low-shot training capabilities, improve algorithm usability and robustness, and collaborate with software engineering, architecture, and product teams to deliver scalable, production-ready, real-time solutions.

Leads Data Science, Network Research, and Asset Classification teams developing highly accurate cyber-physical system visibility solutions. Oversees AI/ML scaling, feature engineering, industrial and medical protocol parsing, data fusion, and passive and active data collection. Collaborates with product and R&D teams to support asset discovery, classification, risk management, segmentation, and threat detection across OT, ICS, and IoMT environments.

Leads the company’s technology organization across Engineering, Product, Data, Infrastructure, and IT. Owns technology strategy, architecture, product roadmap, platform scalability, reliability, security, cloud infrastructure, DevOps, and AI adoption. Partners with executive leadership to align technology initiatives with global business growth, modernize architecture, improve automation and observability, and develop AI-powered products and workflows.

Develop and improve deep learning models for autonomous driving planning, decision-making, and behavior. Own work across research, prototyping, training, evaluation, data pipelines, tooling, debugging, integration, and deployment. Design safety and performance metrics, analyze model failures, improve data and training processes, and validate capabilities in simulation and real vehicles while collaborating with control, simulation, data, and algorithm teams.

Lead technical product strategy and roadmap for AI-driven, hardware-accelerated video analytics products. Define specifications, prioritize features, set performance and cost benchmarks, collaborate with engineering, gather customer feedback worldwide, and translate technical and market inputs into product requirements and designs.

Lead the Payment Fraud product roadmap to maximize approvals and minimize fraud by partnering with engineering, data science, analysts, and sales. Drive new market entry through vertical research, merchant engagement, pilot programs, and performance tracking (approval rates, fraud rates, chargeback impact).

Develop backend services and cyber defense solutions for Microsoft Security. Responsibilities include improving service lifecycles, analyzing complex system behavior, streamlining deployments, reducing costs, collaborating across engineering and security teams, and using AI-assisted development tools. The role requires hands-on backend software development experience, initiative, and the ability to design and operate scalable services in cloud or on-premises environments.

Contribute to design, develop, test, and maintain AI-powered features and services. Implement production-quality code, build agents, prompt and retrieval workflows, run evaluations and monitoring, debug issues, and apply CI/CD, security, and responsible AI practices while collaborating with mentors and cross-functional team members.