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.

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.

The AI Architect will lead AI architectural guidance, hands-on innovation, and establish best practices for AI development while ensuring compliance with regulations.

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.

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 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.

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.

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.

Create and review realistic, locale-specific robotics and natural sciences tasks in Russian to benchmark AI systems. Apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce high-quality gold-standard solutions and expert feedback.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Own and evolve ClickHouse's Python connector and SDK ecosystem; build enterprise-grade integrations with orchestration and transformation tools; design AI/LLM integration patterns for RAG, vector search, and feature pipelines; engage with open-source community; collaborate with Product and Cloud teams to prioritize and deliver robust, high-performance connectors.

Develop, train, debug, and improve computer vision and 3D perception models for ADAS. Own end-to-end ML lifecycle work (data, training, evaluation, iteration), build scalable data pipelines including auto-/pseudo-labeling, and contribute to online (in-car) and offline perception systems for detection, segmentation, tracking and 3D reconstruction.