Top Machine Learning Jobs in Jerusalem

Develop production algorithmic systems that transform inconsistent documents, drawings, imagery, and other data into reliable geospatial intelligence. Responsibilities include problem definition, data collection and processing, relevance and reliability assessment, document and image understanding, coordinate anchoring, evaluation methodology, and production deployment. The role applies computer vision, geometry, classical algorithms, machine learning, and LLM-based approaches while collaborating with mapping experts, surveyors, and researchers.

Own Optibus’s Rail product from zero to one, including discovery, MVP definition, design-partner validation, first customer deployments, roadmap, OKRs, and scalable product strategy. Collaborate with engineering, design, sales, marketing, customer success, and onboarding teams to establish product-market fit, shape go-to-market plans, and deliver successful rail and multimodal transit solutions.

Lead the architecture and implementation of an enterprise data warehouse in AWS. Design scalable data platforms, pipelines, data lakes, and analytical structures using AWS services, Python, Spark, Airflow, Parquet, and Iceberg. Provide technical leadership to data engineers, define technology roadmaps, and collaborate with development, data science, and business teams to support BI, AI, and generative AI initiatives.

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 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.

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.

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.

Designs data architecture, pipelines, governance, and quality standards while developing and deploying production ML models. Owns the full model lifecycle from research and experimentation through monitoring and iteration. Partners with Product and R&D to deliver AI capabilities for vulnerability detection, attack emulation, and security insights, while evaluating build, buy, and fine-tuning options.

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.

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.

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.

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.

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.

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.

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.

Own production optimization of LLM and VLM inference systems, improving latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost. Deploy and benchmark inference engines, build compression and quantization workflows, implement acceleration techniques, diagnose serving bottlenecks, and collaborate with kernel, platform, research, product, and customer teams.

Develop and ship end-to-end machine learning models for autonomous driving behaviors across L2-L4 systems. Own data curation, training, evaluation, experimentation, and on-road validation. Improve model performance through iterative training cycles while addressing real-world safety, quality, and deployment constraints. The role may involve reinforcement learning, transformer networks, automotive systems, model compression, and embedded deployment.

Take end-to-end ownership of specific ADAS features (e.g., High Beam Assist, Blind Spot Monitoring). Define scope and specs, align OEM requirements with internal roadmaps, collaborate with engineering for delivery, participate in testing and validation, mitigate risks, and maintain feature roadmaps to meet production and compliance targets.

Build and scale production machine learning infrastructure, including training and inference pipelines, model deployment, feature generation, registries, monitoring, governance, and CI/CD. Improve reliability, reproducibility, observability, performance, and cost efficiency across ML systems. Collaborate with data scientists, analysts, and engineers to productionize models, establish technical standards, and guide the platform roadmap.