Flying cars are now a reality. With ride-sharing and autonomous vehicles already ubiquitous in society, automatic air taxis have taken to the skies.
So now that the machines are built, the laws are agreed, and the passengers ready and waiting for their flying machines to take them to their next meeting, the system that links up and automates everything in between needs to be flawless.
Meet the Machine Learning Engineer, the expert in building software that connects infinitely large data sets and real-world hardware, and runs completely automatically.
These are the data scientists cum software engineers, the ones who relish diving into huge amounts of information and building automatic digital magic off the back of it. They are the ones who can grasp various different systems, but also work out how to link them all.
They are the experts in training machines to work without humans, understanding at what point there are too many options and decisions to code as a command, and when the computer must simply decide for itself.
To build a system that could automatically run a city on flying cars, you’d need to connect various different pieces of digital information. There’s the navigation system of the cars themselves; there’s the real-time traffic information, and approved air routes used to optimize the travel time; there’s the internet calendars of the passengers dictating when and where they need transport to and from; and there’s the payment systems of all the various parties to be tapped into at the end of every flight.
Making use of an ability to connect the dots
The Machine Learning Engineer needs to know how all these systems work, how their data is structured, what trends lie within, and how to train a computer to make the right decisions at the right time so that the flying vehicle is ready and waiting when a person wants to go somewhere.
They are the curious people who drill down into data sets; they are the big-picture architects linking various oddly shaped entities smoothly together; they are the patient problem solvers constantly re-jigging their training systems to get the computer to always make the right call.
Machine Learning Engineers have to understand various elements of the digital world — from the computer science fundamentals and probability and stats, to data science and system design. They are the ones who are always investigating the latest machine learning libraries created all over the planet, playing with the technology to test its limits and its potential.
They are capable of pulling together something real that works, as well as working purely in theory to understand the overarching solutions.
They are obsessed with finding the simplest, most elegant way of making a system work without them. And in a world of increasing amounts of data, and endless ways to connect it all, building systems to manage that load without a human brain is a crucial skill we desperately need more of.
Illustration: Matthew Hollister
