Top Machine Learning Jobs in Jerusalem

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.

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.

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.

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.

Lead discovery, definition, and delivery of product initiatives across Claroty's CPS security platform. Own product vision and roadmap, prioritize features, collaborate with engineering, design, and GTM teams, and integrate AI/ML capabilities into threat detection solutions to drive adoption and scalability.

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.

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.

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, implement, evaluate, and improve computer vision algorithms for 3D point-cloud processing using classical and machine-learning methods. Work with proprietary LiDAR data to deliver innovative, state-of-the-art solutions for autonomous vehicles and other applications. Collaborate closely with a technical team while designing algorithms, developing software, and potentially optimizing implementations for embedded platforms.

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.

Lead computer vision and AI products from research and definition through development with algorithm and engineering teams. Collaborate with business, marketing, operations, and strategic retail clients to define solutions, manage roadmaps, and deliver products for cashierless shopping, theft prevention, and retail insights. The role requires B2B product management experience, strong analytical and communication skills, customer experience across the US and EU, and a STEM degree.

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.

Lead product vision, strategy, and roadmap for the Velocity TDIR platform. Drive evolution to an AI-powered MDR/Security Operations platform, partner with security and engineering teams, engage customers, prioritize product investments, and measure adoption, analyst efficiency, and market differentiation.

Design, build, and deploy production ML systems for a mobile-gaming loyalty platform using high-volume behavioral data. Create real-time models for marketplace matching, dynamic pricing, personalized recommendations, fraud detection, and game-economy simulation, owning end-to-end delivery and iteration.

As a Full Stack Developer, you will develop and manage web applications, collaborate with Data Scientists, optimize databases, and lead technology initiatives in healthcare.

Lead product development for AI-driven retail solutions from research to delivery, collaborate with R&D, Business, Marketing and Operations, build roadmaps with strategic clients, and manage enterprise customer relationships for computer-vision and ML products.

Design, build, and operate the server side of a large-scale visual moderation platform. Own high-throughput async pipelines for images and videos, multi-tenant systems, and cost-sensitive ML workloads. Make architectural decisions, ship iteratively, and establish practices for testing, observability, reliability, and maintainability while collaborating with engineers and product stakeholders.