Handling scale is a technically challenging problem, if you enjoy it - then take advantage! however sometimes taking a break to work on something else can be more satisfying.
Typically on a "High scale" service spanning hundreds or thousands of servers you'll have to deal with problems like. "How much memory does this object consume?", "how many ms will adding this regex/class into the critical path use?", "We need to add new integ/load/unit tests for X to prevent outage Y from recurring", and "I wish I could try new technique Y, but I have 90% of my time occupied on upkeep".
It can be immensely satisfying to flip to a low/scale, low/ops problem space and find that you can actually bang out 10x the features/impact when you're not held back by scale.
Source: Worked on stateful services handling 10 Million TPS, took a break to work on internal analytics tools and production ML modeling, transitioning back to high scale services shortly.
Typically on a "High scale" service spanning hundreds or thousands of servers you'll have to deal with problems like. "How much memory does this object consume?", "how many ms will adding this regex/class into the critical path use?", "We need to add new integ/load/unit tests for X to prevent outage Y from recurring", and "I wish I could try new technique Y, but I have 90% of my time occupied on upkeep".
It can be immensely satisfying to flip to a low/scale, low/ops problem space and find that you can actually bang out 10x the features/impact when you're not held back by scale.
Source: Worked on stateful services handling 10 Million TPS, took a break to work on internal analytics tools and production ML modeling, transitioning back to high scale services shortly.