Senior Staff Software Engineer - Integration

Stellar Cyber

About The Position

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 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 is not just a senior IC role—we need someone who can independently lead projects end-to-end, pick up whatever knowledge is missing to get the job done, make architectural decisions with confidence, set technical direction, and mentor engineers on the team. As we scale, we need technical leaders who can be the go-to person for design guidance, code review, and engineering growth within their domain—someone whose work has a multiplier effect on the entire team’s output, not just their own. We move fast, give engineers real autonomy, and embrace AI not just in our products but in how we build them. This role carries a dual mandate: 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 architect and 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. 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. Make the architectural calls—system boundaries, API contracts, reliability targets, build-vs-buy trade-offs—that require deep judgment and context no AI can replace. Serve as a technical mentor to the engineering team—through design reviews, pairing sessions, and code reviews—with a focus on growing engineers’ ability to independently own projects and make sound design decisions. Set technical direction and champion engineering rigor across the team: quality, documentation, and best practices in secure coding and distributed systems. 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. Automate repetitive engineering tasks—builds, deployments, monitoring, incident triage—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.

Requirements

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.
Proficiency in Python, Go, or Java with a strong foundation in building and operating microservices in production.
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).
Strong background in distributed systems, message queues (Kafka, RabbitMQ), or orchestration frameworks (Celery, Airflow, or similar).
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.
Track record of mentoring engineers and raising the technical capabilities of a team.
Excellent system design, debugging, and communication skills. You can explain complex trade-offs clearly to both engineers and product stakeholders.

Nice To Haves

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.

Responsibilities

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.
Make the architectural calls—system boundaries, API contracts, reliability targets, build-vs-buy trade-offs—that require deep judgment and context no AI can replace.
Serve as a technical mentor to the engineering team—through design reviews, pairing sessions, and code reviews—with a focus on growing engineers’ ability to independently own projects and make sound design decisions.
Set technical direction and champion engineering rigor across the team: quality, documentation, and best practices in secure coding and distributed systems.
Accelerate your own design and development workflow using LLM-based coding assistants, AI-driven testing, and automated code review.
Automate repetitive engineering tasks—builds, deployments, monitoring, incident triage—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.