Top Remote Jobs in Israel

Integrate embedded application software and algorithm libraries for internal and customer-facing releases. Responsibilities include maintaining Linux software versions, managing Git/GitLab integration branches, coordinating cross-team integration, validating versions, resolving code conflicts, debugging and testing components, and managing releases, features, and CI testing coverage.

Integrate and maintain embedded application software and algorithm libraries for internal and customer-facing releases. Manage Git and GitLab integration branches, coordinate cross-team integration tasks, validate software versions, resolve code conflicts, debug and test components, and manage releases, features, and CI testing coverage across multiple projects.

Lead SOTIF analysis for autonomous vehicle systems by developing safety KPIs, requirements, guidelines, methods, and tools. Collaborate with R&D, validation teams, customers, regulators, and automotive stakeholders to identify and mitigate safety risks, validate system behavior, and promote industry acceptance of Mobileye’s SOTIF methodology.

Own conversion rate optimization for performance marketing landing pages and the paid on-site funnel. Lead the CRO experimentation roadmap, landing page creation, A/B test design and analysis, funnel KPI optimization, dashboards, conversion signal infrastructure, and cross-functional execution with Marketing, Engineering, Design, and BI. Use quantitative and qualitative insights to identify conversion opportunities and rapidly deliver high-impact experiments.

Lead system-level engineering for point-of-care ultrasound products across the full lifecycle. Manage requirements and specifications, drive system verification and validation, coordinate Algorithm, Software, and V&V teams, lead product risk management, resolve complex technical issues, and support program scheduling and technical decision-making. Requires an engineering degree and at least five years of medical device or complex systems development experience.

Leads a hands-on engineering team responsible for cloud-based revenue management products. The role combines coding, code review, architecture, roadmap delivery, hiring, performance management, and team development. The manager partners with Product and cross-functional teams, owns delivery commitments, drives technical quality and continuous improvement, and develops future technical and people leaders. Experience with Azure, FinOps, SaaS production systems, APIs, multidisciplinary teams, and AI-augmented engineering is expected.

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 mechanical design from concept through production for multidisciplinary R&D projects. Create 3D models and drawings, prototype and transfer to production, perform analysis and testing, manage risk and design reviews, and collaborate with global teams to balance performance, manufacturability, reliability, cost, and schedule.

Design and maintain backend services and APIs, enhance performance, and collaborate with cross-functional teams for product integration.

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 frontend quality across the development lifecycle, including requirements review, test strategy, manual and exploratory testing, production monitoring, incident investigation, KPI analysis, and continuous improvement. Collaborate with agile teams to identify risks, validate complex business logic, perform API and data investigations, and contribute to automation and quality tooling. Experience with monitoring systems, SQL, REST APIs, browser debugging, and live-environment validation is required.

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.