So close to being great - Abhorrent management - Software Engineer Testlio Employee Review

The renewed company focus allows teams to prioritize effectively and maintain alignment. The leadership team is grounded, motivated, and highly collaborative. The product strategy successfully embraces AI, positioning the company to define the future of AI testing.

The volume of organizational change over the past year was a bit high. While these pivots were clearly made to ensure both near and long-term success, so much change can lead to change fatigue. I believe that leadership maintaining the current level of transparency will be key as the org continues to stabilize under the new focus.

There is no growth and learning.

- Management lacked consistency and long-term direction. Priorities often changed without clear communication. - Processes were not well defined and varied significantly from one project to another, creating confusion and inefficiencies. - Knowledge sharing was limited, making it difficult for new team members to get up to speed quickly. - Quality of leadership and technical expertise varied across projects and teams. - Heavy dependence on contractors resulted in frequent team changes and loss of project knowledge. - Documentation was often incomplete or outdated, requiring testers to spend extra time gathering information. - Career growth opportunities and promotion paths were not clearly defined. - Feedback and performance expectations were sometimes unclear. Project assignments could end abruptly with little notice, creating uncertainty for contractors. - Communication across multiple time zones could delay decisions and issue resolution. - Test environments and access management occasionally caused unnecessary delays. - Lack of standard QA practices across projects made it difficult to establish consistent quality standards. - Limited opportunities to influence product decisions since QA teams generally worked as external contributors rather than part of the client's core team. - Resource planning was sometimes reactive rather than proactive, leading to workload fluctuations.