Pros: Decent learning experience that moves fast

Cons: Not confident in leadership, not a rocketship.

Pros: - Good engineering and research talent. - Good people to work with. - Decent place to learn about AI, or at least it used to be. - Good middle-management, reasonable expectations and perf reviews. - Good reputation in the industry that can be a good stepping stone to something else, or as a foot in the door of the AI industry. - Cool swag and events.

Cons: - Immature executive leadership who let their Stanford PhD reputations carry them - Despite what they say, they don't care about their people; this is obvious when someone leaves, voluntarily or not. Outside their immediate pod, they tend to just disappear. Only longer tenured employees get a mention, typically in a random Slack message. Otherwise, you find out by suddenly finding them deactivated on Slack without any explanation - Poor strategy and long term decision making that was very slow to react to the rapid swing towards gen-AI, and then it was way too little too late - Core business, which seemed very promising, dried up - Dramatic pivot to new data as a service direction of which is not what anyone signed up for, and they're essentially working 9-9-6. Working so intensely, they're doing nothing but creating engineering tech-debt, and never cleaning any of it up. - Super competitive market for the new direction, and they're extremely far behind already. Very unlikely they can catch up - Work is uninteresting, just grinding - especially post-pivot - Aimless design efforts

Pros: Good culture, nice people, and a talented team.

Cons: Tight deadlines, resource constraints, and hard to coordinate work