QA Engineer
Weekly Rate: $7,000/week
Overview
The Quality Assurance Engineer establishes testing strategies and ensures system quality across all components. This role validates accuracy, performance, and reliability through comprehensive testing protocols, ensuring the system meets all requirements before deployment.
This critical quality assurance role ensures that automated judging systems meet the exacting standards required for competitive sports applications, where accuracy and reliability directly impact athlete experience and competition integrity.
Key Responsibilities
Test Strategy and Planning - Define comprehensive testing strategies that cover all aspects of the squat tracking system from computer vision accuracy to system performance. Develop test plans that ensure thorough validation across different deployment scenarios and edge cases.
Automated Testing Framework - Build robust test automation infrastructure that enables continuous testing throughout the development lifecycle. Implement automated regression testing to prevent feature degradation and ensure consistent quality.
Performance and Load Testing - Design and execute performance testing protocols that validate the system meets sub-200ms latency requirements under various load conditions. Conduct stress testing to identify breaking points and ensure graceful degradation.
Accuracy Validation Protocols - Develop rigorous accuracy measurement protocols specific to squat detection and rep counting. Create validation datasets that represent diverse athlete populations, squat techniques, and environmental conditions.
Edge Case Testing - Systematically identify and test edge cases including occlusion scenarios, lighting variations, and unusual athlete movements. Ensure the system handles unexpected situations gracefully without compromising accuracy.
Field Testing Coordination - Organize and coordinate real-world testing at actual HYROX venues with live athletes. Gather feedback from judges and officials to validate system usability and effectiveness in competition environments.
Hardware Compatibility Validation - Test system compatibility across different camera models, edge computing devices, and network configurations. Ensure consistent performance across the variety of hardware deployments.
Security and Vulnerability Testing - Conduct comprehensive security testing including penetration testing and vulnerability scanning. Validate data protection measures and ensure compliance with privacy regulations.
Bug Management and Prioritization - Establish and manage the complete defect lifecycle from discovery through resolution. Prioritize issues based on severity, impact, and risk to ensure critical problems are addressed first.
Release Validation and Certification - Lead release validation processes ensuring all quality gates are met before deployment. Provide formal certification that releases meet accuracy, performance, and reliability requirements.
Required Skills
QA Engineering Excellence demonstrates strong experience developing comprehensive testing strategies for complex systems where accuracy and reliability directly impact operational success. They have built automated testing frameworks that effectively identify regression issues before they impact production environments. Their experience includes coordinating testing efforts across multiple development teams while maintaining consistent quality standards throughout rapid development cycles.
Test Automation and Performance Validation combines solid test automation knowledge with performance testing experience that validates system behavior under realistic load conditions. They understand the unique testing challenges of computer vision and machine learning systems where traditional testing methodologies require adaptation. Their performance testing experience includes identifying system bottlenecks and validating that performance meets demanding latency requirements under varying operational conditions.
Computer Vision and ML Testing Approaches shows understanding of how to validate accuracy and robustness of pose estimation algorithms across diverse operational scenarios and edge cases. They have developed effective testing approaches for machine learning systems where conventional unit testing methods are insufficient for ensuring system reliability. Their knowledge of CV/ML testing methodologies helps identify potential failure modes that could impact system accuracy during competitive events.
Field Testing and Coordination involves practical experience coordinating real-world testing scenarios that accurately simulate actual competition conditions with live athletes and operational staff. They understand how to organize meaningful field testing that provides valuable validation while minimizing disruption to ongoing event operations. Their process management and issue tracking skills ensure problems are appropriately prioritized and resolved based on their potential impact on competition integrity.
Phase Allocation
The QA Engineer begins with partial involvement during Beta phase to establish testing frameworks and early validation procedures. Full-time engagement during Gamma and Delta phases coincides with intensive system testing and field validation activities. The role continues at reduced capacity during Full Release to support production monitoring and issue resolution.
| Phase | Weekly Rate | Allocation | Duration |
|---|---|---|---|
| Alpha | - | 0% | - |
| Beta | $3,500/week | 50% | 12 weeks |
| Gamma | $7,000/week | 100% | 8 weeks |
| Delta | $7,000/week | 100% | 10 weeks |
| Full Release | $3,500/week | 50% | 12 weeks |
Deliverables
Test Strategy Documentation. Comprehensive testing strategy covering all aspects of system validation including functional, performance, accuracy, and field testing approaches. This documentation establishes clear testing objectives, methodologies, and success criteria while defining roles and responsibilities throughout the testing lifecycle.
Automated Test Suites. Production-ready automated testing frameworks covering unit, integration, and end-to-end test scenarios with continuous integration support. These suites enable rapid regression testing and consistent quality validation while reducing manual testing effort and improving test coverage across all system components.
Performance Test Results. Detailed performance validation reports demonstrating system compliance with latency, throughput, and scalability requirements under various load conditions. These results include stress testing, endurance testing, and spike testing that validate system behavior under extreme conditions encountered during major competitions.
Accuracy Validation Reports. Comprehensive validation of computer vision accuracy including squat detection rates, false positive/negative analysis, and edge case performance metrics. These reports provide statistical confidence in system accuracy while identifying scenarios requiring additional algorithm refinement or training data augmentation.
Bug Reports and Metrics. Structured defect tracking with clear reproduction steps, severity classifications, and resolution tracking throughout the development lifecycle. Quality metrics including defect density, escape rates, and mean time to resolution provide insights into system quality trends and development process effectiveness.
Test Datasets. Curated collections of video footage, sensor data, and system inputs representing diverse competition scenarios and edge cases. These datasets enable consistent validation across system versions while supporting algorithm training and performance benchmarking throughout the project lifecycle.
Release Certifications. Formal quality attestations confirming system readiness for production deployment based on comprehensive testing and validation activities. These certifications provide stakeholder confidence while documenting that all quality gates have been successfully passed before system release.
Testing Procedures. Detailed step-by-step procedures for conducting manual testing, field validation, and user acceptance testing activities. These procedures ensure consistent test execution across different testers and venues while maintaining comprehensive documentation of testing coverage and results.
Success Criteria
Success in the QA Engineer role is measured through quantifiable quality metrics and comprehensive system validation.
Automation and Coverage. The position requires achieving greater than 95% test automation coverage across all system components and comprehensive edge case coverage that validates system behavior under challenging conditions. These metrics ensure consistent quality validation and reduce manual testing overhead.
Quality and Performance. Success criteria include maintaining a defect escape rate of less than 0.1% and ensuring zero critical bugs reach production environments. All accuracy targets must be validated through rigorous testing protocols, and performance requirements including sub-200ms latency must be consistently met across all testing scenarios.