Software Development Engineer in Test

Socure Socure · Vertical AI · United States · Remote · Tech

Seeking a Senior SDET to enhance quality engineering by integrating automated functional validation, production health monitoring, and AI-driven failure analysis for disaster recovery and production environments. The role focuses on ensuring system availability, functionality, continuous validation, and self-diagnosis capabilities, advancing Socure's shift towards autonomous, resilient quality systems.

What you'd actually do

  1. Design and implement automated functional health checks for DR and production environments using synthetic transactions and API validation.
  2. Build continuous validation pipelines that verify end-to-end business workflows such as authentication, transactions, and system integrations.
  3. Develop intelligent alerting mechanisms based on functional failures and customer-impacting behavior, not solely infrastructure metrics.
  4. Integrate observability signals including logs, metrics, and traces with automated test frameworks to improve system visibility and diagnosis.
  5. Develop AI/ML-driven approaches to detect failure patterns, correlate issues across services, and identify probable root causes.

Skills

Required

  • 5+ years of experience in QA Automation, SDET, Software Engineering, or a related technical discipline.
  • Strong experience building and maintaining automated test frameworks, including tools such as Playwright, Jest, SuperTest, and REST API testing frameworks.
  • Experience working in cloud environments, preferably AWS.
  • Familiarity with observability and monitoring platforms such as Datadog, New Relic, CloudWatch, Splunk, or similar tools.
  • Strong programming skills in TypeScript, Python, Java, or similar languages.
  • Experience designing end-to-end test strategies for distributed systems and production-like environments.
  • Strong problem-solving skills, with the ability to analyze failures across application, infrastructure, and workflow layers.

Nice to have

  • Experience with synthetic monitoring, production validation, or proactive health-checking systems.
  • Exposure to AI/ML techniques for anomaly detection, log analysis, or failure correlation.
  • Experience with CI/CD pipelines, release automation, and validation gates.
  • Understanding of microservices architecture, distributed system failure modes, and incident management practices.
  • Familiarity with SRE concepts such as SLIs, SLOs, error budgets, or production-readiness practices.

What the JD emphasized

  • AI/ML-driven approaches
  • intelligent alerting mechanisms
  • automated remediation actions
  • customer-impacting behavior

Other signals

  • AI/ML-driven approaches to detect failure patterns
  • intelligent alerting mechanisms
  • automated remediation actions