Senior / Staff Software Engineer - Integration Team

Join a fast-growing global leader in cybersecurity, trusted by some of the biggest names in the industry. In addition to some of the world’s largest enterprises and government agencies, more than 30% of the world’s top MSSPs rely on our platform. We’re at the forefront of protecting organizations against sophisticated cyber threats using cutting-edge AI and automation technologies. Our culture is built on diversity, openness, and collaboration, fostering creativity and innovation that drives real impact in the market.

Position Overview

We are looking for a Senior or Staff Software Engineer to take ownership of one or more integration domains on our Automation-Driven Open & Unified SecOps Platform (powering our AI-driven SIEM, NDR, Open XDR, and Multi-Layer AI).

This role requires delivering robust, production-grade integrations that support our growing customer base, while driving the architecture forward so the platform can scale to the next level. You will deliver microservice-based solutions spanning both traditional and AI-native integration patterns—REST, GraphQL, streaming APIs, MCP, and LLM-powered automation—connecting our platform with a wide range of cybersecurity tools and third-party services. You will also design, develop, and maintain the parsing and normalization components that turn raw security data from hundreds of sources into the unified schema our detection and analytics engines rely on.

Please note, as part of our interview process, we will be inviting successful finalists for an on-site interview to meet with our team.

What You Will Own

We need someone who focuses on the work AI cannot do—and uses AI to amplify everything else.

The work only you can do

• Own one or more integration domains end-to-end: roadmap, architecture decisions, trade-offs, and delivery. You identify what needs solving, scope it, and drive it to completion.

• Own and evolve shared integration frameworks, such as the parser framework: identify patterns worth abstracting, design the primitives other engineers build on, improve performance on hot paths, and raise the reliability and testability bar across the team’s output.
• Make the design calls that require judgment—schema mapping, normalization trade-offs, how to handle ambiguous or malformed data, when to generalize versus when to special-case, when to evolve the framework versus work around it.
• Partner with detection and data teams to make sure ingested data serves downstream use cases, not just passes validation.
• Mentor junior engineers through design discussions and code reviews, helping them grow into independent owners.

What you’ll use AI to amplify

• Accelerate your own design and development workflow using LLM-based coding assistants, AI-driven testing, and automated code review.
• Use LLMs to analyze unfamiliar log samples, propose initial parsing rules, and bootstrap new integrations faster—while applying human judgment to catch what AI gets wrong.
• Automate repetitive engineering tasks—builds, deployments, monitoring, incident triage, parser regression testing, schema diffing—using AI and scripting, so the team spends more time on high-value work.
• Champion AI adoption across the team: help colleagues integrate AI tools into their daily work, build internal tooling where it adds leverage, and measure the efficiency gains.