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We are looking for a Linux Internals Software Engineer for our Tel Aviv R&D center. You will be part of a team that designs and develops the Linux internals components of our revolutionary endpoint security product for the Linux platform
More information about Cortex XDR product can be found here.
Key Responsibilities
Design and implement innovative capabilities for a market-leading product, deployed on a range of platforms from bare-metal to the cloud.
Develop critical components in the Linux internals parts of the product, including user and kernel-mode OS-related infrastructures in both C and C++.
Utilize cutting-edge Linux technologies for real-time event collection, anti-malware, anti-exploitation, and network filtering.
Lead features by collaborating and coordinating with other development and QA teams to ensure alignment on shared objectives.
Uphold and contribute to a team culture that values software development best practices and high standards for quality.

As a Senior IT SRE Engineer, you will be a key player in ensuring the reliability, scalability, and performance of our critical IT infrastructure. You will leverage SRE principles and an automation-first mindset to build and maintain resilient hybrid cloud environments. This role is ideal for a candidate who thrives in a fast-paced, innovative setting and is passionate about solving complex challenges with cutting-edge technology.
Key Responsibilities
Provision, configure, and support resilient hybrid cloud deployment architectures using an Infrastructure-as-Code framework.
Proactively collaborate with development teams to ensure new applications are production-ready, scalable, and reliable from inception.
Develop and maintain tools and frameworks to automate operational tasks, including deployment, monitoring, and recovery.
Conduct thorough root cause analysis of production issues and implement preventative measures to improve system resilience, demonstrating strong problem-solving skills.
Manage CI/CD platforms, Linux infrastructure, and contribute to capacity planning and operational runbooks.
Design and implement proactive service monitoring, alerting, and trend analysis to maintain service availability and performance SLAs.
Participate in an on-call rotation to support critical applications and services, responding to and resolving incidents efficiently.
Contribute to comprehensive documentation related to infrastructure design, deployment, and operational procedures.

This is a high-ownership, builder-first Sr. Software Engineer role. You will design, build, and ship AI-integrated data systems from concept through production - owning outcomes end-to-end, including deployment, monitoring, cost, and business impact.

We are seeking a candidate who views AI tooling as a fundamental force multiplier in their daily engineering process. This position is central to our transition into an AI-native function, requiring an individual capable of making decisive, pragmatic architectural choices on reversible matters to maintain momentum. We need an experienced builder of production-grade, data-centric systems who is obsessed with delivering customer value and possesses a deep, curious enthusiasm for the transformative potential of AI.

What You Will Build
AI-Native Systems Development. Design, build, and own scalable data and ML pipelines, backend services, and AI-powered capabilities that are part of the platform's production decision-making layer. AI and ML components are runtime dependencies in this role - not research projects or experiments. Candidates will have strong back end and data engineering skills to thrive in this space.
Daily Shipping. Decompose complex work into safely mergeable increments and ship them daily. Treat large, multi-day pull requests as a risk to momentum. Use feature flags, canary releases, and rollback architecture to manage risk through isolation - not through avoidance.
AI-Augmented Engineering Workflow. Leverage AI-assisted development tooling (code generation, automated testing, architecture prototyping) as a core workflow multiplier. Evaluate and experiment with emerging AI tools and frameworks with direct hands-on engagement. Bring technical depth to AI fluency - architecture and capability tradeoffs, not surface-level awareness.
End-to-End Ownership. Own your work from design through production deployment, operational monitoring, and business impact measurement. Accountability extends beyond the feature to CI/CD pipeline health, observability, cost efficiency, and domain-level outcomes.
Architectural Decision-Making. Make pragmatic, timely architectural choices that balance modern AI and data technologies with reliability, cost, and delivery speed. Distinguish reversible vs. irreversible decisions and move forward without waiting for consensus on the former. Document decisions in lightweight ADRs and own the outcomes.
Cross-Functional Collaboration. Partner with product, design, infrastructure, and GTM teams to translate customer and business needs into technical solutions. Operate with business awareness - understand how your systems impact revenue, customer outcomes, and strategic priorities.