Top Machine Learning Jobs in Israel

Develops and deploys advanced machine learning and AI models using large-scale, real-time data. Responsibilities include defining business problems, exploring data, designing algorithms, building scalable production architectures, and collaborating with engineering and product teams. The role also involves innovation, knowledge sharing, mentoring, and making architectural decisions. Experience with AdTech, real-time ML, deep learning, LLMs, or reinforcement learning is advantageous.

Develop and productionize machine learning and data science solutions for internal business partners. Responsibilities include stakeholder collaboration, statistical modeling, time-series forecasting, full ML model lifecycle ownership, monitoring and automation, and prototyping RAG, agentic, and knowledge-graph solutions. The role requires independently taking projects from proof of concept to production while communicating findings and managing cross-functional relationships.

Develop computer vision and deep learning algorithms for detection, segmentation, tracking, and re-identification using large-scale video data. Prototype and evaluate models, analyze failures, optimize inference pipelines, and build production-quality Python solutions. Collaborate with R&D, backend, infrastructure, and QA teams to integrate algorithms into production systems. Support data collection and annotation, apply AI-assisted development tools, and improve model performance, scalability, and robustness.

Own the fraud and risk product roadmap by researching emerging fraud patterns, analyzing customer and proof-of-concept data, identifying signal and model gaps, and defining scalable product capabilities. Partner with customers, Sales, Solutions, Data Science, and Marketing to evaluate data providers, improve fraud detection, reduce false positives, guide product execution, and communicate fraud intelligence and product insights.

Own end-to-end data science for fraud and risk detection products, including LLM and classical ML modeling, prompt engineering, evaluation, experimentation, monitoring, and production deployment. Partner with engineers on real-time streaming and batch systems while defining data-quality metrics, observability, labeling strategies, and performance improvements for adversarial, imperfectly labeled data.

Design and develop scalable Java backend services for a merchant risk platform. Build microservices on AWS, solve performance and business-logic challenges, own features from design through production, review code and architecture, and collaborate with product, data, and engineering teams. Contribute to AI-first development practices, platform architecture, engineering improvements, and merchant-classification capabilities.

Own go-to-market strategy, positioning, messaging, product launches, sales enablement, thought leadership, and competitive intelligence for YouAppi’s mobile marketing, retargeting, and CTV platform. Partner with Product, Sales, Growth, and Leadership to translate technical capabilities and performance data into compelling customer narratives, strengthen differentiation, and support business growth.

Manages a multidisciplinary team of data engineers, scientists, and analysts developing statistical and machine learning solutions on large-scale data. Leads end-to-end data pipeline development from research through production, ensures process quality and reliability, collaborates with product and R&D teams, engages business stakeholders and customers, hires team members, and contributes to product strategy.

Build and operate data and ML infrastructure for multimodal sensor data, supporting customer algorithm teams across annotation, training, evaluation, deployment, and observability. Develop pipelines, data environments, model onboarding, and evaluation workflows; work directly with customers to solve technical needs and translate insights into platform improvements. Seed data products and collaborate with algorithms, product, software, and DevOps teams to establish company-wide data and MLOps standards.

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.

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 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.

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.

Serve as the senior ML and AI technical expert for customers and field engineering. Architect and optimize production-grade ML, GenAI, agentic, RAG, MLOps, and data engineering solutions on Databricks. Support technical sales, mentor solution architects, communicate complex concepts, guide model deployment and monitoring, and influence product strategy through customer feedback.

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).

Leads the Performance Management engineering organization, overseeing design, development, testing, deployment, scalability, capacity planning, operational support, and product quality. Establishes engineering standards, manages delivery and resources, incorporates customer requirements, and leads a globally distributed team. The role also drives adoption of AI and agentic technologies, recruits and develops engineering talent, and partners with business leadership to deliver scalable SaaS products.

Research and develop machine-learning-based physical-layer algorithms for 5G and future wireless products. Design, train, optimize, and benchmark neural networks for channel estimation, signal detection, beamforming, and decoding, targeting real-time embedded deployment. Responsibilities span research, simulation, Python/PyTorch/TensorFlow and MATLAB prototyping, specification, implementation support, integration, and customer release. The role evaluates ML approaches against traditional DSP methods for accuracy, latency, computational cost, and system performance.

Leads bioinformatics research and development for cancer genomics products. Analyzes large whole-genome and next-generation sequencing datasets, develops machine learning architectures, statistical algorithms, mathematical models, and high-throughput data pipelines, and improves clinical performance and platform optimization. Collaborates with R&D, production, clinical, software, and data science teams while documenting algorithms, research findings, processes, and quality metrics.

Build and operate ML- and LLM-powered systems for Stampli’s SaaS platform. Responsibilities include model training, evaluation, production integration, monitoring, scalable backend APIs, distributed systems, microservices, asynchronous processing, event-driven architecture, CI/CD, observability, and cloud infrastructure. The role partners with product and engineering stakeholders, leads technical design reviews, makes architectural tradeoffs, and delivers AI capabilities at scale.

Develop and deploy state-of-the-art deep learning and computer vision algorithms for automated vehicle inspection. Responsibilities include creating proof-of-concepts, transitioning research into production systems, benchmarking models on large datasets, applying data-centric AI methods, and optimizing real-time inference across edge and cloud environments. The role collaborates with infrastructure, MLOps, and hardware teams.

Build and productionize LLM-powered and machine-learning pipelines for product classification, normalization, taxonomy mapping, and entity matching across billions of records. Develop agentic workflows, evaluation frameworks, and scalable Spark/Databricks data solutions. Balance accuracy, cost, and latency while collaborating with Product, data engineering, data science, and R&D teams to move solutions from proof of concept into production.

Lead system-level hardware testability architecture for NVIDIA networking products across the product lifecycle. Define hardware, software, firmware, fixtures, and cabling test solutions; maximize test coverage; create specifications and production test flows; present testing architectures; support production ramp-up and engineering changes; assess DFT risks; and coordinate cross-functional teams, contract manufacturers, and suppliers.