Stellar Cyber

Open XDR platform for security operations

Senior / Staff Software Engineer - Integration Team

Backend EngineeringConfirmed live in the last 24 hours

Full-timeRemote in TaiwanNo salary listed

About the Job

Requirements

A bachelor's or master's degree in Computer Science, Engineering, or a related field is required.
At least 5 years of backend software development experience, including integration, data parsing, or log processing work, and at least 1 year as a technical lead or proven experience taking full ownership of a project are required.
Strong proficiency in at least one general-purpose programming language, such as Python, Go, Java, or C++, and a track record of writing production-quality code in it are required.
Experience with API design and integration, including REST, GraphQL, and streaming, and secure API patterns such as OAuth, API keys, and rate limiting is required.
A solid understanding of common log and data formats—including JSON, XML, CSV, syslog, key-value, and unstructured text—regular expressions, data normalization, and schema design is required.
Demonstrated regular use of AI tools such as Copilot, Cursor, Claude, or ChatGPT to accelerate engineering workflows in production work is required, along with judgment to determine when AI output needs human expertise.
Strong problem-solving skills and clear communication with engineers and non-technical stakeholders are required.

Responsibilities

Own one or more integration domains end-to-end, including the roadmap, architecture decisions, trade-offs, and delivery; identify problems, scope them, and drive them to completion.
Own and evolve shared integration frameworks such as the parser framework by identifying patterns to abstract, designing primitives for other engineers, improving hot-path performance, and raising reliability and testability across the team's output.
Make design decisions about schema mapping, normalization trade-offs, ambiguous or malformed data, generalization versus special-casing, and framework evolution versus workarounds.
Partner with detection and data teams to ensure ingested data supports downstream use cases, not merely validation.
Mentor junior engineers through design discussions and code reviews to help them become independent owners.
Use LLM-based coding assistants, AI-driven testing, and automated code review to accelerate design and development workflows.
Use LLMs to analyze unfamiliar log samples, propose initial parsing rules, and bootstrap integrations, applying human judgment to identify errors.
Automate repetitive engineering tasks—including builds, deployments, monitoring, incident triage, parser regression testing, and schema diffing—with AI and scripting.
Champion AI adoption across the team, help colleagues integrate AI tools into daily work, build internal tooling where useful, and measure efficiency gains.
Deliver production-grade integrations and advance platform architecture through microservice-based solutions using traditional and AI-native patterns, including REST, GraphQL, streaming APIs, MCP, and LLM-powered automation.
Design, develop, and maintain parsing and normalization components that convert raw security data from hundreds of sources into the unified schema used by detection and analytics engines.

Desired Qualifications

Experience in cybersecurity domains such as SOAR, EDR, SIEM, XDR, firewalls, IDS/IPS, or cloud security services, and familiarity with data emitted by security tools.
Experience building and operating microservices in production.
Hands-on experience with large-scale data processing, real-time pipelines, or event-driven architectures such as Kafka or Spark.
A strong background in distributed systems, message queues such as Kafka or RabbitMQ, or orchestration frameworks such as Celery or Airflow.
Familiarity with cloud platforms such as AWS, Azure, or GCP, their logging services such as CloudWatch, Azure Monitor, or Cloud Logging, and containerized deployments using Docker, Kubernetes, or Helm.
Experience with AI service integration, including LLM APIs or embedding services, or AI-native integration patterns such as MCP, function calling, or agent orchestration frameworks; alternatively, hands-on experience building systems that incorporate LLMs or AI services into workflows or product features.
Hands-on experience using LLMs for log analysis, parser generation, or schema inference, or building internal tooling that applies AI to data engineering workflows.

Senior / Staff Software Engineer - Integration Team

Stellar Cyber · Taiwan (Remote) · Full-time · Posted 2026-10-01

Workplace: remote

Department: 050000 Engineering

Description

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 or 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 role requires 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 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. You will also design, develop, and maintain the parsing and normalization components that turn raw security data from hundreds of sources into the unified schema our detection and analytics engines rely on.

Please note, as part of our interview process, we will be inviting successful finalists for an on-site interview to meet with our team.

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.
Own and evolve shared integration frameworks, such as the parser framework: identify patterns worth abstracting, design the primitives other engineers 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.
Partner with detection and data teams to make sure ingested 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 design and development workflow using LLM-based coding assistants, AI-driven testing, 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 engineering tasks—builds, deployments, monitoring, incident triage, parser regression testing, schema diffing—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
5+ years of backend software development experience, including integration, data parsing, or log processing work, with 1+ years as a technical lead or proven experience in taking full ownership of a project.
Strong proficiency in at least one general-purpose programming language (e.g., Python, Go, Java, or C++), with a track record of writing production-quality code in it.
Experience with API design and integration (REST, GraphQL, streaming), including secure API patterns (OAuth, API keys, rate limiting).
Solid understanding of common log and data formats (JSON, XML, CSV, syslog, key-value, unstructured text), regular expressions, and data normalization and schema design.
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.
Strong problem-solving skills and clear communication with both engineers and non-technical stakeholders.

Preferred Qualifications

Experience in cybersecurity domains (SOAR, EDR, SIEM, XDR, firewalls, IDS/IPS, cloud security services) and the kinds of data security tools emit.
Experience building and operating microservices in production.
Hands-on experience with large-scale data processing, real-time pipelines, or event-driven architectures (e.g., Kafka, Spark).
Strong background in distributed systems, message queues (Kafka, RabbitMQ), or orchestration frameworks (Celery, Airflow, or similar).
Familiarity with cloud platforms (AWS, Azure, GCP) and their logging services (CloudWatch, Azure Monitor, Cloud Logging), and containerized deployments (Docker, Kubernetes, Helm).
Experience with AI service integration (LLM APIs, embedding services) or 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.
Hands-on experience using LLMs for log analysis, parser generation, or schema inference—or building internal tooling that applies AI to data engineering workflows.

Why Join Us

Work at the forefront of cybersecurity innovation within a dynamic, fast-growing team.
Opportunity to significantly influence and shape the integration architecture of a next-generation SecOps platform powered by AI and automation.
Competitive salary and ample career growth opportunities.

Apply

Apply at Stellar Cyber

About the Company

Stellar Cyber

Open XDR platform for security operations

Stellar Cyber provides an Open XDR security platform that unifies data from multiple security tools to help enterprises, MSSPs, and public sector organizations detect and respond to threats. The platform uses a scalable data lake to ingest and transform data from different tools, creating a single view for faster investigation and enabling automated detection and response. It differentiates itself by delivering end-to-end visibility across a diverse set of tools and automating cross-tool workflows, with subscription licensing and additional threat hunting and security consulting services. Its goal is to help organizations improve their security posture by reducing risk and speeding up detection and response without adding burden to security teams.

Company size: 51-200
Company stage: Late Stage VC
Total funding: $112.8M
Headquarters: San Jose, CA
Founded: 2015

Recently Posted Jobs

Explore more opportunities at Stellar Cyber

See more jobs