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: The flexibility of tasks at any time

Cons: The late onboarding time taken by them

Pros: Smart, helpful people and in general a decent work environment

Cons: Leadership takes forever to make decisions, and then pivots every couple of months, completely changing product direction. Recent layoffs are due to poor planning. Work is always under tight deadlines, with little appreciation. There is very low morale and high attrition rates, with very little upside on the stock and no 401k matching.