Company vehicle Gas card Purchase card Career growth opportunities
Health benefits
Pay
Coworkers (T.H. especially)
Workplace etiquette
Communication
The office is hectic and not an ideal space for trainings or getting work done. Lots of open space. It's hard to think, let alone pay attention to a training video (even with headphones on) because there are people all over the place and usually right next to you shouting, swearing, or on phone calls.
All employees should take workplace harassment classes, or sexual harassment classes, again. They should also be reminded of the workplace etiquette policy in regard to language and acting accordingly, as customers may be in and out of the office for various reasons.
The new office is terrible.
I was asked about projects and topics, such as polymorphism. I was also asked how I tested projects. There were a few STAR-style questions as well. I talked to only the main engineering manager. Overall, it was a relatively simple interview.
When this company is not interested in you, they simply ghost you. Your application will remain stuck in the “In process (HR department)” status for an entire month without a single update, even if you send follow-up messages. From a company of this
Phone call followed by on-site interview. The phone call tests basic AI/ML questions covering data analysis, model selection, finetuning, and deployment. Some questions address the basics of neural networks, optimizers, and loss functions, etc.
I was asked about projects and topics, such as polymorphism. I was also asked how I tested projects. There were a few STAR-style questions as well. I talked to only the main engineering manager. Overall, it was a relatively simple interview.
When this company is not interested in you, they simply ghost you. Your application will remain stuck in the “In process (HR department)” status for an entire month without a single update, even if you send follow-up messages. From a company of this
Phone call followed by on-site interview. The phone call tests basic AI/ML questions covering data analysis, model selection, finetuning, and deployment. Some questions address the basics of neural networks, optimizers, and loss functions, etc.