Pros
Your work directly helps people find jobs and helps employers staff critical roles. You can see the impact of your models through A/B tests and real hiring outcomes, not just abstract metrics. The ML team works closely with the business, so projects are focused on moving the needle rather than just research. Good technical challenges with large-scale data (billions of signals), modern ML stack, and exposure to the full lifecycle from data engineering to production deployment. Team is collaborative and supportive, leadership is clear about priorities, and there's real ownership over your work.
Cons
Like many mid-size tech companies, resources can be tight so you need to be scrappy and wear multiple hats. The pace can be intense when there are competing priorities. Austin office is smaller so if you're looking for a huge ML team with tons of specialized roles, this might not be it. Some legacy systems and technical debt to work through.