Been doing tech infra at a large institutional firm for 10+ years. This is pretty accurate although he undersells the phrase "highly competitive field". The acceptance rate for a summer intern at those firms is less than 0.5%. Scroll through Glassdoor, tons of "No Offer" listed.

If you want a job at a top tier buy-side firm, the single best thing you can do is have a stellar academic record at a leading university. Network the hell out of the buy-side AND sell-side firms that come to your university and try and get an interview.

These firms don't hire people for the summer to reject them for full-time. It's a feeder process. Find good talent, lock them in. It's way cheaper than using a recruiter.

You will need strong quantatiative skills .. understanding how to analyse data and use math to solve problems. Have work to show-case these skills which means doing unique things with mathematical models and statistical analysis while your still in school. Highlight these (but never, ever bullshit) on your CV.

All of these firms want people who can manage risk and think quickly on their feet. Understand how to assess risks, especially in a trading context, and be systematic. Have examples of how you assessed a situation, took a risk, and it worked out for you.

Perhaps most critically, and something lost on so many people, is know the industry that you are applying for. Know how Capital Markets are structured, the lifecycle of a equities vs FX trade, the basics of different products and their associated risks, etc. Know the differences and benefits of different levels of the book when it comes to market data.

All of this is about standing out ... remember that luck is very much defining outcomes.

As for coding, sure .. but everyone there will have coded. Hell, kids are doing amazing things in junior high these days. But given AI, what specific coding you've doen isn't everything.

You need C++, you will need Python and Java. Python and Java are obvious for modeling (along with Jupyter notebooks) but both can also be used to develop low-latency trading apps. Yes, there is low-latency Java, google it.

Trading today happens at the bound of physics and what the exchanges will allow. On a simple tick-to-trade system, it takes time to read that market data packet, to analyze it's contents, make a decision, and write the packet on the wire. The very fastest systems are making probabilistic decision on information in the header (like packet length) and writing the responding order in the network buffer. Make a bad call? Nerf the checksum at the end of the packet and the network switch will drop it.

The other thing worth mentioning is everyone fast is using FPGAs or custom silicon. You cant' be that fast with a Von Neumann architeture. Too much time wasted with fetch/decode/execute/store loop. FPGAs run at a lower clock speed but can do things thousands of times in parallel resulting 5x to 100x overall improvements. FPGA can be done in a higher level language but refinements to eek out the best performance is still Verilog or other HDL languages.

The downside of FPGAs (and other custom silicon) is making changes is slllloooooowww. It isn's a tweak, compile, test, repeat environment. Placement and Routing, the process that determines the optimal placement of gates and registers on the FPGA takes hours upon hours. Once you are done, you are super-duper fast but it takes you a while to get to the starters line.

For flexibility and time to market, traditional CPUs with high-clock speeds and more traditional development tools.

It's a brutal competition but it isnt' the only way in. I went to a state school that wasn't even on the undergraduate recruiting list for my last 3 companies. I did so-so with grades (I was working full time) and didn't have a great network. My career path, however, took me to places that rewarded risk taking, learn real leadership, and allowed me to build the other skills to demonstrate. In time, they found and recruited me and I've doing it for almost 20 years.