As my title says, looking for inputs on whether it becomes harder for ML leadership to rise in technical leadership ranks beyond a certain level than it is the case for general software dev leadership. And is this different at big tech vs startups? **By ML leadership, I am referring to leading teams that prototype build and deploy models in production, not (just) research teams. By general SD, I am referring to teams building any one of non-ML backend applications (the most common background of top leadership), frontend, infrastructure/ops, and even data engineering (in the spectrum, DE and analytics is next to ML in terms of this glass ceiling issue) I do see some (not exact) parallels of this effect further up as well - for example, it becomes harder for CTO to becomes CEO which is not the case for Product or even Sales folks who have a greater breadth of business exposure. Appreciate DM offers to talk about similar experiences/thoughts you may have had. #engineering TC: 220K yoe: 5
Generally, tenured ML engineers have their background in academia. So they grow to become more traditional managers trying to control too hard. That might be stopping them from rising quicker. But I have seen many cases where managers with SDE background tend to manage better if given an ML team.
As you stated, as long as you have a good understanding of the product, company and you feel strongly about a strategy that ensures it’s growth and success - then no, no ceiling. I’ve seen leaders that started in documentation and QA make it quite far in a company based on those attributes. Don’t know about startups.
True that. I also wonder if this perceived glass ceiling issue is due to a combination of: - ML being a newer field - ML teams being generally fewer/smaller (compared to backend application, for example) within a company - hence lesser span of its leaders - ML leaders, even if they had risen above math and code, are still less skilled/interested in general strategies