Stellar Cyber · Remoto · United States
Join our team as a Principal Software Engineer, where you will take ownership of one or more integration domains on our Automation-Driven Open & Unified SecOps Platform. This role requires a strong background in distributed systems, API design, and integration, as well as proficiency in Python, Go, or Java. You will be responsible for making architectural decisions, mentoring engineers, and driving the technical direction of the team. Additionally, you will have the opportunity to automate repetitive engineering tasks and champion AI adoption across the team.
- Strong background in distributed systems, message queues (Kafka, RabbitMQ), or orchestration frameworks (Celery, Airflow, or similar)
- Excellent system design, debugging, and communication skills. You can explain complex trade-offs clearly to both engineers and product stakeholders
- Demonstrated ability to use AI tools (e.g., Copilot, Cursor, Claude, ChatGPT) to meaningfully accelerate engineering workflows—not just experimentation, but regular use in production work. You have the judgment to know when AI output is trustworthy and when it needs human expertise
- Proficiency in Python, Go, or Java with a strong foundation in building and operating microservices in production
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- 8+ years of backend software development experience, with 3+ years in a Staff-level or tech lead role where you owned a domain or led a team
- Deep experience with API design and integration—both traditional (REST, GraphQL, streaming) and AI service integration (LLM APIs, embedding services)—including secure API patterns (OAuth, API keys, rate limiting)
- Track record of mentoring engineers and raising the technical capabilities of a team
- Experience in cybersecurity domains: SOAR, EDR, SIEM, XDR, or similar
- Hands-on experience with large-scale data processing, real-time pipelines, or event-driven architectures
- Familiarity with cloud platforms (AWS, Azure, GCP) and containerized deployments (Kubernetes, Helm)
- Experience with AI-native integration patterns—such as MCP (Model Context Protocol), function calling, or agent orchestration frameworks—or hands-on experience building systems that incorporate LLMs or AI services into workflows or product features