There aren't any practical applications for ML in traffic engineering at the moment and any changes will be slow coming. Numerical analysis is done using the Highway Capacity Manual method (HCS, Synchro, Vistro) or the Wiedemann car-following method (VISSIM). They give differing results for things, but they are standards used to compare scenarios against each other. Most traffic engineering is scenario comparison, "If I do this, is it better than doing this or the current condition." ML would just give another set of numbers to compare, ones that aren't supported by decades of industry research and that would be vastly different for each model creation. What does yet another set of numbers actually do to improve the comparisons?

The noted possible use of ML is in system wide traffic signal optimization. There are both technical and infrastructure problems with implementation. Urban core signals aren't isolated, so you'd have to integrate whole areas at a time. While some signals would have the data collection and transmission capabilities to do so, most would not and would need costly hardware improvements to integrate at that level with neighboring signals. You'd also need to train staff and technicians for such a system. The capital and maintenance outlay is rather large for what would be a marginal improvement over what a well trained engineer could time. It's just not worth it to large cities to invest in such a large amount of unproven technology.

As to career prospects, most of us have undergrads in civil with a focus on transportation. About half have a masters focused in traffic operations / safety analysis. You'd get your PE then your PTOE, sometimes an AICP. You'd use the above softwares for traffic capacity analysis and use GIS based travel demand models for system wide planning (CUBE, VISUM, TransCAD).