Pros
**Teammates** All my teammates were knowledgeable and great to work with. We had good diversity within the team. Good understanding and support from everyone in my team
Cons
I am writing this in detail to inform others about the situation inside ASUS-AICS. This is one of the most poorly managed companies I have encountered. I caution anyone from joining here The team builds health related software. Despite boasting a research team and ML engineers, not a single AI feature has been added to the system in the past two years. ***Key Personnel have left under mysterious circumstancs*** The AI director and CVP/CTO were both reported to be on personal leave, but it’s the same story used for both. The CVP is like the CEO of the company. He has left without much remorse stating family related break as reasons. Recently, three engineering managers and two-thirds of the ML engineers have left. Three out of five AI researchers are leaving as well. These positions are quickly filled with other members to satisfy numbers for government tax matters. ***Employment Practices*** The company uses PIP (Performance Improvement Plan) as a tactic. They even warn that everyone is on thin ice, with a single P0 bug potentially leading to PIP. ***Product Vision*** No AI features have been released in the past two years. Multiple projects were halted or abandoned after just a few months of development. The CVP, vetos all the decisions over every AI product, makes decisions despite lacking knowledge in AI or ML. Management In the past six months, the management including the chief scientist and GM have poorly guided the team. No AI product features have been released, and no research papers published. Projects are often stopped prematurely under the pretext of “lack of innovation.” There is no clear vision for AI or ML. The company is leveraging EDB funds for setting up an AI team and having ML knowledge within AICS with no clear goals. They need to maintain a certain headcount within the AI team to satisfy needs ***Lack of resources*** The management does not provide researchers and ML team necessary resources to do your work effectively. The resources are less than what is available in the local universities.