Top Machine Learning Jobs in Israel

The Sales Engineer will support enterprise sales across EMEA by leading technical discovery, solution design, product demonstrations, proofs of concept, RFP responses, and technical presentations. The role advises prospects on fraud prevention, chargeback mitigation, payment optimization, and risk management. It partners with Sales, Product, Engineering, Implementation, and Customer Success teams, translates customer requirements into technical specifications, and supports handoffs. Strong expertise in ecommerce, payments, fraud prevention, APIs, and enterprise stakeholder communication is required.

Lead product strategy for a performance advertising platform, optimizing bidding logic, serving algorithms, prediction systems, and marketplace dynamics. Partner with Data Science, R&D, analytics, and business teams to translate complex algorithms into scalable capabilities, improve advertiser ROI and conversion rates, and balance advertiser demand with publisher supply. Design and interpret large-scale A/B tests, identify optimization opportunities, and guide cross-functional product launches.

Own the vision, strategy, roadmap, and execution for monday CRM Sales. Lead development of AI-powered agentic experiences that automate sales workflows, surface insights, and improve deal execution. Collaborate with engineering, design, AI/ML, data, analytics, sales, customer success, and GTM teams. Use customer feedback and quantitative metrics to prioritize products across desktop and mobile.

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.

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.

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.

Analyze product, brand, and category data to identify quality gaps and opportunities. Define quality metrics, build dashboards and checks, maintain ground-truth datasets and labeling guidelines, evaluate LLM and ML model outputs, and support taxonomy development. Partner with Product, Engineering, and customer-facing teams to investigate data issues, validate new sources, prioritize improvements, and translate findings into product requirements.

Lead and support AI transformation and digital strategy projects for global organizations. Analyze business challenges, identify AI and data opportunities, develop roadmaps and business cases, assess AI maturity and technologies, translate business needs into technical requirements, facilitate workshops, and collaborate with stakeholders to drive AI adoption and data-driven decision-making.

Design and implement agentic platforms, AI workflows, LLM and RAG solutions, evaluation frameworks, and production-grade AI systems. Prepare large-scale data for training and inference, integrate AI with existing products, and collaborate cross-functionally to identify opportunities. The role requires Python proficiency, modern AI expertise, software development experience, and practical experience building AI agents, workflows, and LLM-based solutions.

Lead discovery, definition, and delivery of product initiatives for Claroty's platform. Own product vision and roadmap, integrate AI/ML capabilities for threat detection, manage end-to-end lifecycle, prioritize features, and collaborate with engineering, design, sales, marketing, and customer success to drive scalable platform adoption.

Leads large-scale, multidisciplinary AI processor R&D programs from architecture through production and customer delivery. Coordinates hardware, algorithms, firmware, embedded software, toolchains, verification, and systems teams of 30+ engineers across global sites. Owns planning, schedules, risks, quality, KPIs, change control, PMO processes, customer communication, and executive reporting for NPU and edge AI products.

Lead and manage the sales team while closing user acquisition advertising deals with app developers and mobile advertisers. Build international relationships, secure and expand UA budgets, negotiate campaign contracts, manage the sales pipeline, identify upselling opportunities, and exceed revenue targets using performance metrics such as CPI, CPA, and ROAS.

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.

Develop and deploy large-scale machine learning, deep learning, and AI models to detect mobile advertising fraud. Responsibilities include mining massive datasets, identifying evolving fraud patterns, building end-to-end ML pipelines, productionizing models, monitoring performance, and collaborating with product, engineering, and business teams. The role also evaluates emerging AI technologies and contributes to explainable, high-impact fraud prevention solutions.

Leads MyHeritage’s scientific roadmap in genetics, genomics, and health-related products. Manages senior scientists and data scientists while guiding development of scalable algorithms and production pipelines for sequencing analysis, variant interpretation, carrier screening, polygenic risk scores, and genetic genealogy. Collaborates with engineering, product, and data teams to deliver user-facing features and communicate scientific findings. Requires deep computational genomics expertise, clinical sequencing experience, machine learning knowledge, and scientific leadership.

Develop and maintain computer vision and signal-processing algorithms in C++ and Python for autonomous driving products. Responsibilities span research, production software engineering, new feature development, product maintenance, and validation using metrics, tests, and real-world data. The role requires collaboration across teams and familiarity with machine-learning workflows, including datasets, training, and evaluation.

Leads end-to-end Data and AI projects from requirements and solution architecture through implementation and deployment. Translates business needs into AI-driven solutions, coordinates data scientists and technical teams, oversees model testing and monitoring, and supports automation and digital transformation initiatives. The role also involves data visualization, stakeholder management, business-oriented presentations, and consulting across complex enterprise environments.

Build and own end-to-end features for an AI-powered email security platform, implementing frontend UIs (React/TypeScript) and backend services (Python), writing tests (Playwright/E2E), designing scalable APIs, collaborating on architecture and product decisions, and improving the engineering lifecycle and tooling.

Leads cross-functional system product engineering projects from requirements through end of life. Responsibilities include integration planning, risk mitigation, bug prioritization, manufacturing data analysis, automated test development, quality and yield improvement, production scaling, failure analysis, test validation, and coordination across hardware, software, firmware, chip design, operations, and suppliers.

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.

Leads GeoEdge’s malvertising detection strategy and security research organization. Responsibilities include identifying emerging threats and evasion techniques, driving AI and agentic detection innovation, translating research into production capabilities, ensuring detection quality, publishing original research, representing the company at security conferences, and advising executives, customers, and partners on the threat landscape.

Own end-to-end product analytics for Tavily, measuring funnels, adoption, retention, conversion, and monetization. Partner with Product, R&D, Growth, GTM, and leadership to define metrics, prioritize roadmaps, evaluate experiments, uncover behavioral drivers, and guide product strategy. Build trusted datasets using SQL, dbt, and Python; apply statistical and predictive modeling; monitor product performance; and communicate actionable recommendations to stakeholders.

Lead end-to-end planning and execution for complex REM map development programs supporting ADAS and autonomous driving. Coordinate product, algorithm, development, and validation teams; track milestones, risks, dependencies, and changes; provide technical and operational leadership; drive mitigation plans; and communicate program status and recommendations to senior stakeholders.

Conduct research on AI security agent behavior by forming hypotheses, designing experiments, analyzing traces and failure modes, and building research harnesses. Develop AI projects from proof of concept through production, including algorithms, pipelines, memory, context, and evaluation systems. Monitor emerging models and research, publish findings, and present at industry conferences while supporting customer-facing AI infrastructure.

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