Senior / Staff Software Engineer - Parser Team

Join a fast-growing global leader in cybersecurity, trusted by some of the biggest names in the industry. Besides many enterprises and government agencies, nearly 30% of the world’s top MSSPs rely on our platform, and that number is growing every day as more companies recognize the value of next-generation security solutions. We're at the forefront of protecting organizations against sophisticated cyber threats using cutting-edge AI and automation technologies.

We offer a highly competitive compensation package on par with industry leaders like Google and Microsoft, ensuring our team is rewarded for their expertise and dedication. We are fully remote, providing the flexibility to work from anywhere in Taiwan. We are rooted in transparency and openness, without rigid hierarchies. We are feedback-driven, encouraging open communication and innovation at every level. Our culture is built on diversity, openness, and collaboration, fostering creativity and innovation that drives real impact in the market.

Stellar Cyber is looking for a Senior or Staff Software Engineer to own parser development on our Automation-Driven Open & Unified SecOps Platform (powering our AI-driven SIEM, NDR, Open XDR, and Multi-Layer AI). Parsers are the front door of our platform—every security event from firewalls, endpoints, cloud logs, and SaaS tools flows through the components you build before it can be detected, correlated, or acted on. The quality, coverage, and performance of your work directly shapes what the rest of the platform can see.

Stellar Cyber needs someone who treats parsing as an engineering domain to own, not a ticket queue to grind through—someone who can independently drive the parser framework forward, pick up new log formats and vendor quirks quickly, make sound design decisions on schema and normalization, and raise the bar for the engineers around them. We move fast, give engineers real autonomy, and embrace AI not just in our products but in how we build them—parser development is one of the areas where AI-assisted workflows deliver the biggest leverage.

You will design, develop, and maintain parsing and normalization components that turn raw security data from hundreds of sources into the unified schema our detection and analytics engines rely on—handling large volumes, diverse log formats, and constantly evolving vendor outputs.

Please note, as part of our interview process, we may invite candidates for an in-person interview to meet with our team. 

• →Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• →5+ years of software engineering experience with a focus on data parsing, integration, or log processing.
• →Strong proficiency in Python, Java, Ruby, or C++.
• →Deep familiarity with common log formats and data structures (JSON, XML, CSV, syslog, key-value, unstructured text).
• →Strong command of regular expressions and other pattern-matching techniques.
• →Solid understanding of data normalization, schema design, and transformation principles.
• →Experience integrating with APIs, web services, and streaming data sources.
• →Demonstrated ability to use AI tools (Copilot, Cursor, Claude, ChatGPT) to meaningfully accelerate engineering workflows—regular use in production work, not just experimentation. You have the judgment to know when AI output is trustworthy and when it needs human expertise.
• →Working understanding of cybersecurity concepts and the kinds of data security tools emit.
• →Strong problem-solving skills and clear communication with both engineers and non-technical stakeholders.
  

PREFERRED QUALIFICATIONS

• →Experience with cybersecurity tools and platforms (firewalls, IDS/IPS, EDR, SIEM, cloud security services).
• →Familiarity with cloud platforms (AWS, Azure, GCP) and their logging services (CloudWatch, Azure Monitor, Cloud Logging).
• →Experience with big data and streaming technologies (Kafka, Spark, Hadoop, or similar).
• →Experience with containerization and orchestration (Docker, Kubernetes).
• →Hands-on experience using LLMs for log analysis, parser generation, or schema inference—or building internal tooling that applies AI to data engineering workflows.
• →Familiarity with AI-native integration patterns such as MCP (Model Context Protocol), function calling, or agent frameworks.