Product QA Engineer

About the role

We are seeking a QA/QC Engineer responsible for the structured testing and validation of SAR and EO imagery products, AI/ML model outputs, and the associated image analysis applications.

This is a testing-first role with end-to-end ownership of product QA, responsible for:

• Validation of SAR/EO imagery and AI/ML model outputs
• End-to-end application and workflow testing
• Smoke, functional, exploratory and regression testing across releases
• Structured defect tracking and verification
• QA automation, reporting and release validation
• Maintaining repeatable, audit-ready QA processes

The role is expected to operate independently from development and processing teams, providing an objective assessment of product quality and release readiness.

Key Responsibilities

A. Image & Model Output Validation

• Inspect SAR and EO imagery for distortions, inconsistencies and other quality issues.
• Validate model outputs including detections, classifications, annotations and overlays against defined benchmarks.
• Identify false positives, missed detections, localization errors and recurring model failure patterns.
• Perform regression validation when models, processing pipelines or module configurations are changed.
• Provide structured QA inputs and independent assessment of release readiness prior to internal release or customer delivery.

B. End-to-End Product & Release Testing

• Test complete image analysis workflows from data ingestion through processing, analysis and visualization.
• Validate application functionality, UI workflows, overlays, annotations, layers and analysis outputs.
• Perform functional, smoke, exploratory and regression testing across standalone and server-based product versions.
• Maintain reusable smoke and regression test suites to support frequent product releases.
• Verify fixes and ensure changes do not introduce regressions elsewhere in the system.

C. Test Planning and Automation

• Develop and continuously improve structured test cases, regression suites, checklists and QA workflows covering imagery, model outputs and application functionality.
• Identify gaps in test coverage, tooling and processes and implement improvements to increase QA efficiency and coverage.
• Identify repetitive QA activities suitable for automation and build Python-based scripts and utilities where feasible.
• Maintain reusable test templates and frameworks to support rapid release cycles.

D. Defect and Issue Management

• Identify, document and classify defects with clear reproduction steps, evidence, severity and priority.
• Track defects through resolution, retesting and closure.
• Maintain structured visibility of issues by product version, module, defect type, status and customer/deployment location.
• Track recurring, unresolved and deferred issues across releases and highlight systemic quality concerns.

E. QA Reporting & Documentation

• Maintain structured reports for feature, smoke, regression and release testing.
• Maintain test cases, execution records, checklists, defect logs and supporting evidence.
• Establish reusable reporting templates to support rapid release cycles and consistent QA practices.
• Generate summaries of open issues, recurring defects, regression status and release readiness.
• Ensure QA results are reproducible, traceable and audit-ready, including support for customer-facing quality documentation.