VnV Engineer

At Trigo, we’re revolutionizing the way people shop with our cutting-edge AI-driven technology, enabling cashier-less stores and advanced theft prevention systems.

We’re looking for a skilled and motivated Verification & Validation (V&V) Engineer to join our multidisciplinary engineering team.

In this role, you’ll design and execute validation strategies ensuring Trigo’s advanced computer vision and backend systems meet the highest standards of quality, performance, and reliability. You’ll collaborate closely with software, hardware, and research teams to bring our innovative store automation solutions from lab to production.

A day in the life

• Design and implement test plans and validation procedures for Trigo’s AI-based perception and backend systems.
• Develop and maintain automated testing frameworks and tools using Python.
• Create test scripts to validate end-to-end system performance, functionality, and reliability.
• Collaborate with development, product, and operations teams to ensure smooth integration and release cycles.
• Analyze test data, identify issues, and work with developers to resolve defects efficiently.
• Participate in live store validation and field testing to confirm system robustness in real-world environments.
• Contribute to the continuous improvement of testing methodologies and processes.
  
  

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, or a related field - Must
• Strong Python coding skills for automation, scripting, and data analysis.
• 2+ years of experience in Verification & Validation, QA, or Test for complex software or embedded systems.
• Solid understanding of testing methodologies, regression testing, and validation metrics.
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI) and version control systems (Git).
• Familiarity with Linux-based systems and networking fundamentals.
• Strong analytical, problem-solving, and communication skills, with a detail-oriented mindset.
  
  

• Experience testing computer vision, machine learning, or sensor-based systems.
• Background in large-scale distributed or real-time data processing environments.
• Familiarity with Docker, Kubernetes, or cloud infrastructures (AWS, GCP, or Azure).
• Understanding of hardware/software integration and system-level testing.
• Exposure to Agile methodologies and test-driven development (TDD) principles.