Top Machine Learning Jobs in Jerusalem

Design, develop, and deploy cloud-native, high-scale backend and Big Data systems for security intelligence. Optimize data pipelines, integrate machine learning models into production, and advance AI initiatives from proof of concept to production features. Collaborate with product, UX, data science, and engineering teams to build tools that detect and address cyber threats. The role requires backend or data engineering experience, distributed systems expertise, cloud proficiency, and practical knowledge of machine learning, LLMs, agents, and generative AI.

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.

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.

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.

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.

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.

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 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.

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.

Lead architecture for an enterprise data warehouse in AWS, designing scalable data infrastructure, pipelines, data lakes, and analytical systems. Select and optimize AWS data services, formats, and processing technologies; establish the technology roadmap; mentor data engineers; and collaborate with developers, data scientists, and end users on BI, AI, and GenAI solutions. Candidates must also pass reliability, security, and polygraph testing.

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.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Designs, develops, deploys, and optimizes large-scale data pipelines across cloud and on-premise environments. Ingests high-volume data, analyzes datasets with Data Scientists, and builds data solutions for data warehouse and AI projects. The role requires Python, Spark, dbt, pipeline orchestration, container platforms, advanced data formats, AWS data services, and BI tool integration, along with strong analytical, collaborative, and independent working skills.

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.

As a CBRN expert at Alice.io, you will evaluate and create prompts for Generative AI systems, ensuring their safety and accuracy in CBRN contexts. You'll provide expert feedback and stay updated with relevant research and regulations.

Lead product roadmap and specifications for a B2B, on-premise, AI-driven video analytics product. Define performance, scalability, and cost benchmarks for hardware-accelerated systems, collaborate with engineering, gather customer feedback worldwide, and translate technical requirements into hands-on product designs and releases.

Leads the architecture, development, and deployment of enterprise machine learning, Generative AI, NLP, and RAG solutions on AWS. Designs scalable MLOps platforms, data pipelines, feature stores, vector search systems, and model monitoring. Advises enterprise customers, leads technical workshops, collaborates with architects and data scientists, and mentors engineers. Requires advanced Python and ML framework expertise, AWS AI/ML services experience, big data and database knowledge, customer-facing leadership, and fluency in Hebrew and English.