[Hiring] Senior / Staff Software Engineer @Stellar Cyber

Role Description

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.

What You Will Own

• Own the parser framework: identify patterns worth abstracting, design the primitives parser authors 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.
• Drive high-impact parser integrations end-to-end where framework-level thinking is needed, setting the pattern that others follow.
• Partner with detection, data, and integration teams to make sure parsed 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 parser development using LLM-based coding assistants, AI-driven test generation, 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 parser work—regression testing, schema diffing, sample ingestion—so the team spends more time on hard problems.
• Help the team integrate AI tools into their daily parser workflows and measure the efficiency gains.

Qualifications

• 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.
• 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.