Note: The job is a remote job and is open to candidates in USA. Stellar Cyber is a fast-growing global leader in cybersecurity, trusted by many enterprises and government agencies. They are seeking a Senior or Staff Software Engineer to own parser development for their Automation-Driven Open & Unified SecOps Platform, focusing on the design, development, and maintenance of parsing components that handle diverse security data.
Responsibilities
- 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
- 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
Skills
- 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
- 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
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