Ask HN: Applied ml vs. Sde in an ML platform team for career growth

People who have been in the industry for a while, what would you recommend a junior starting out his career to do. I have an applied scientist and an sde in an ML team offer from Amazon.

The SDE role would involve around building the infra / services to scale the ML models and deal with things like metrics, logging, alarms, etc.

The applied scientist role would be an ML role in which I get to train models and write inference code in production. This would involve reading a lot of papers.

I do not have a PhD but have a strong master's background and do think that I'll be able to do both jobs well.

I have been told that distributed systems is mostly a solved problem and ai would offer rapid growth but would like to hear the opinion of people who have had few years in the industry.

My long term goal is to move into leadership roles like VP, CTO of small companies where I can create impact and define product vision. Can choosing ML over systems affect that?