Top Machine Learning Jobs in Israel

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.

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.

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.

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.

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.

Design, develop, and debug backend software for distributed metrology systems; define specifications; collaborate with internal and external stakeholders; perform testing and troubleshooting; produce documentation and reports; influence architecture and deliver complex features within Agile R&D teams.

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.

Own a portfolio of conversational AI products from opportunity identification through launch, scaling, and iteration. Define success metrics, monitor performance, improve conversational quality, and manage hallucination and renter feedback metrics. Translate partner, operations, and user feedback into roadmaps, PRDs, and Jira tickets. Collaborate closely with Engineering and Data Science on experiments, labeling, evaluation loops, engine improvements, and new AI capabilities. The role requires hands-on execution, deep product expertise, and comfort with ambiguity.

Lead complex, cross-functional technology programs across AI, software, robotics, vehicle hardware, infrastructure, simulation, data, and product teams. Own project delivery, improve efficiency, coordinate stakeholders, manage risks and dependencies, align programs with business goals, and drive continuous improvement. The role requires strong technical judgment, systems thinking, communication, influence, and experience delivering complex software or technology programs in fast-paced environments.

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.

Define and implement networking software architecture for high-performance Ethernet switch platforms supporting AI and ML workloads. Explore networking protocols, ASIC capabilities, open-source drivers, and embedded software stacks; develop simulation-based proofs of concept; write technical requirements; and collaborate with R&D and validation teams on next-generation networking systems.

Design, build, and improve machine learning software products using large-scale textual data, NLP, deep learning, LLMs, and generative AI. Develop models, analyze data, create production-ready prototypes, integrate services, and monitor quality and performance through evaluation and metrics. Collaborate with product, engineering, and research teams to deliver data-driven talent solutions.

Owns cross-functional delivery of Sunbit’s credit risk decisioning platform across Card, BNPL, and emerging products. Responsibilities include coordinating ML, engineering, business, compliance, legal, and operations stakeholders; managing requirements, roadmaps, backlogs, dependencies, launches, experimentation, and performance monitoring; and improving underwriting automation, approval decisions, pricing, loss management, regulatory integrity, and customer outcomes.

Own Sedric’s AI reference architecture across services, agents, retrieval systems, evaluation pipelines, and infrastructure. Define reusable patterns and standards, lead architecture reviews, evaluate vendors and foundation models, and shape the technology roadmap. Build production agentic systems, governance frameworks, and evaluation harnesses while partnering with Product, Engineering, customers, and executives. The role combines hands-on delivery with organization-wide technical leadership, including shipping LLM improvements and optimizing latency and reliability.