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GenLogs is a transportation-technology company building next-generation truck intelligence through a nationwide network of sensors and proprietary data. The company delivers real-time, high-fidelity insights into freight movement for commercial supply-chain customers and public-sector agencies, with a mission to strengthen America's logistics backbone, combat freight fraud and cargo theft, and provide visibility into commercial motor vehicle activity across major freight corridors.
The Engineering team builds and sustains end-to-end systems powering the country's most advanced commercial-vehicle sensing and intelligence network, from roadside hardware and computer vision pipelines to distributed cloud infrastructure and real-time data services. The team values technical rigor, speed of execution, and durable infrastructure that delivers mission-critical impact.
As a Senior QA Automation Engineer, you will combine the judgment of a senior QA engineer who understands the platform and business deeply with the engineering skills to turn that understanding into automated integration and end-to-end test suites. You will use AI tools as a core part of your workflow to design, generate, and maintain tests at a pace manual approaches cannot match. Your work directly supports the move toward daily deployments backed by reliable regression gates, where QA owns the release decision.
Key responsibilities include: learning the GenLogs platform end-to-end including products, data flows, customers, and the freight and transportation domain; reviewing specs and requirements early to identify gaps and define clear acceptance criteria; translating business rules and product specs into test strategies and acceptance criteria before code is written; designing, building, and maintaining automated integration and E2E test suites across ReactJS portals and Python/NextJS APIs; building and evolving a two-tier testing approach with fast mocked suites running on every pull request plus scheduled E2E suites against real backends using managed seed data; using AI coding agents and LLM-based tools to generate tests from specs, expand coverage, triage failures, and keep suites healthy; integrating automated suites into CI/CD pipelines as regression gates protecting every release; owning the release gate by defining quality criteria, reporting on risk, and making clear go/no-go recommendations; validating data accuracy and integrity using SQL against large-scale datasets; investigating defects and production issues with precise reproduction steps and root-cause insight; partnering with product, engineering, and data teams to raise quality standards and make testability part of every design discussion; tracking quality metrics such as escaped defects, regression trends, and release stability; and tracking and reducing flaky tests, test debt, and coverage gaps.
REQUIREMENTS:
- 5+ years of experience in software Quality Assurance, with at least 3 years focused on test automation
- Strong programming skills in a modern language: Python, Java, Javascript, or Typescript
- Hands-on experience with E2E frameworks such as Playwright or Cypress
- Solid experience with API and integration testing (pytest, requests, Postman, or similar)
- Proven, practical use of AI tools (Claude Code, Cursor, Copilot, or similar) to write, maintain, and scale automated tests
- Experience integrating test suites into CI/CD pipelines and working with containerized environments
- Strong SQL skills for data validation and test data management
- Experience with test data strategies, including seeding, isolation, and handling non-idempotent operations
- Exceptional attention to detail and a habit of questioning assumptions in requirements and behavior
- Excellent written and verbal communication skills, with the ability to explain risk clearly to technical and non-technical audiences
- High effectiveness working in a remote and distributed team
PREFERRED QUALIFICATIONS:
- Experience with spec-driven development, where specs drive code, tests, and documentation
- Experience testing data-heavy or distributed systems, geospatial data, or computer vision outputs
- Background in logistics, transportation, or supply-chain technology
- Experience building internal AI-assisted tooling or agent workflows for QA
- JavaScript/TypeScript experience
