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Product QA Engineer - SAR & EO Image Validation

Job Description - Product QA Engineer - SAR & EO Image Validation

About GalaxEye

GalaxEye is a Space-Tech startup pioneering the world's first Multi-Sensor Earth Observation Satellite, integrating SAR (Synthetic Aperture Radar) and MSI (Multi-Spectral Imaging) on a single platform. As we move towards building a constellation of indigenous satellites, we are also developing advanced data platforms that fuse satellite data, AI analytics, and geospatial intelligence.

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.



Requirements

Required Qualifications:

        Bachelor’s/Master’s degree in Remote Sensing,
Geoinformatics, GIS, Physics, Electrical Engineering, Computer Science or a
related field.

        2–3 years of experience in QA/testing roles,
preferably involving image, data-heavy or software products.

        Experience with manual, functional, regression
and end-to-end testing.

        Experience or familiarity with SAR and/or
Electro-Optical imagery analysis.

        Familiarity with image analysis/GIS tools such
as QGIS, SNAP, ENVI or equivalent.

        Basic Python scripting skills for test
automation and data validation.

        Strong documentation, reporting and
defect-tracking skills.

        Strong analytical thinking, attention to
detail and systematic problem-solving ability.


Preferred:

        Experience testing AI/ML-based image analysis
or computer vision outputs.

        Exposure to model accuracy validation,
benchmarking or dataset QA.

        Experience building automated QA or regression
testing utilities.

        Familiarity with version-controlled release
testing.

        Experience testing standalone and server-based
applications.

        Experience supporting customer deployments,
UAT, acceptance testing or audit processes.

        Experience in startup or fast-iteration
product environments.

        Familiarity with geospatial, satellite
imagery, remote sensing or defence applications.



Benefits

  • Hands-on experience working with SAR, EOl
    imagery and AI-driven image analysis.

  • Opportunity to own and build QA processes,
    test frameworks and automation
    for a growing product.

  • Exposure to end-to-end product testing, rapid
    release cycles, customer deployments and audit-ready QA
    .

  • Work at the intersection of software,
    geospatial technology, computer vision and space-tech
    .

  • High ownership and the opportunity to influence
    product quality directly and release readiness.


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