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I got my PhD in mid-to-late 2014 in Heavy Ion Physics (colliders with gold/lead/copper/uranium in addition to protons). We wrote a lot of code in c++ for on-det
by frob 8y ago
I got my PhD in mid-to-late 2014 in Heavy Ion Physics (colliders with gold/lead/copper/uranium in addition to protons). We wrote a lot of code in c++ for on-detector selection, data-harvesting, and ultimate analysis and graphics rendering. Due to the dearth of jobs in the area and my spouse having a great, well-paying job locally, I decided to join the startup world. I found a little company that wanted a generalist (i.e. data, software, ui, wrench turning) and became employee #10. Three weeks later we begin the acquisition process with one of the FAANGs. Fast forward a month and a half and I'm now a SWE there with no formal CS training. Additionally, I'm automatically a L4 due to my PhD. This leads to pain. I'm doing the best I can to get work done, but I have a physics mindset where right is 100x more important than fast. That's the exact opposite of working at one of these companies. First half: meets expectations with an informal note that I was on the edge but they understood my position. Second half: meets most. One of the biggest punches to the gut in my life. I start fearing being fired every day (I work in an at-will state but I had yet to understand the costs associated with hiring and firing an employee). I overwork myself to get to two halves in a row of meets all with ALMOST exceeds (with a note of, "if only it weren't for that one week in the half where you shut down due to stress"). I go through a couple of reshuffles until I somehow end up on top of the stack due to some combination of attrition and management issues and get that L5 promotion. I've been riding that for a bit now and I'm coming out on the other side of this journey with a much better understanding of the software development cycle, modern technologies and frameworks, MVPs, product-focused thinking, rapid iteration, the value of good tooling and IDEs, how fast can sometimes be more valuable than perfect (with the right caveats), and a strong level of ML experience on both the product and training sides.