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When you set off to become a developer, you probably dreamed of working on self-driving cars, artificial intelligence, world-changing apps, or any number of other amazing products. However, there’s a lot of learning and beginner developer work you need to do before you can get there. This can be draining and can make you lose sight of what got you excited to be a developer in the first place.
When knee-deep in a tough concept or a product function that’s fighting back, Bytesize is here to give you 5-minutes to shake it off and let your imagination fly once more.
The New Space Age
The Space Race in the late 20th century exploded the engineering job market as thousands of workers in Russia and the United States turned their expertise to the stars. Many of the great gadgets and technologies made during that time have become staples in our modern life, like microwave ovens, velcro, and solar panels. This focus on space inspired the imagination of the next generation of engineers.
However, many governments decided to divert their space funding at the turn of the century due to shifting perceptions of space travel.
More recently in the 2010s, space travel has reentered the minds of both consumers and business leaders. With the advent of private space companies like SpaceX or Blue Origin, space travel is once again seeing a boom.
Their integrations with modern AI and digital software have led to smarter, cheaper rockets. These companies have made great innovations and now set ambitious goals of commercialized space tourism and missions to Mars in the next 10 years.
These companies and those at NASA will need a new generation of talent to help them reach such heights. This means thousands of high-paying and exciting jobs are available not just for engineers but programmers and developers too.
Let’s take a look at some of the programmer jobs you could land to participate in the New Space Age.
1. Automation Tester:
Before any rocket leaves the ground, several teams of engineers have run hundreds of simulated tests on everything from the boosters all the way to the tiniest coupling. NASA used to test these pieces in live environments, but that was time-consuming and expensive. However, they can’t test the pieces less as a failure could cost countless lives and millions of dollars.
Now, automated simulation tests allow engineers to measure anything they want and repeat those tests hundreds or thousands of times with just a click. Each new rocket design, like a commercial space vessel, will need thousands of tests to ensure that the passengers are safe and that any new additions are working as expected.
Automation Testers design the simulated tests important aspects of design like the durability of hull materials to automatic damage control protocols. Your main duties will be to use test scripts to develop tests and to analyze/report results back to the engineering team for incorporation into the next design. Each test you run works to protect the lives of astronauts and takes space vehicles one step closer to perfection.
2. Cybersecurity:
While shuttle technology has become more advanced, so too has the technology of hackers. Cyberattacks are prevalent in all areas of government and business as hackers try to find small weaknesses to abuse for profit.
Space companies are often the target of ransomware attacks that disrupt the use of the rocket or systems surrounding the rocket until the company pays a ransom. These companies also have expensive secrets like patents or experimental prototypes that hackers could sell for a huge profit.
As space travel becomes more popular, these attacks will become more common and costly. Companies will need to hire their own force of ethical hackers and encryption experts to combat the hackers. You’d consistently create and fine-tune security access protocols to ensure the network is secure and safe for travel.
3. Data Scientist:
One thing that hasn’t changed in space travel is the focus on data collection. Whether it’s a sensor to track fuel usage or a camera snapping pictures of the cosmos, space travel generates a lot of data.
The next generation of space travel will need data scientists skilled in big data analysis that can parse and translate immense amounts of data. Those that work with engineers will need to analyze a wealth of data from different sources across a craft to develop a birds-eye view of what’s happening and where improvements could be made. Those working with scientists will need to be experts in data cleaning and be able to recognize emerging trends for future investigation.
For either, you’ll need expert knowledge of Matplotlib and other Python data science tools. Your day-to-day duties will call you to work with cutting-edge findings in engineering and science. This is one of the most important jobs in modern space travel because data scientists are at the frontline to help us uncover the secrets of our universe.
4. Machine learning engineer:
Once we have our heaps of cosmic data, we need something that can use it. Scientists will want to look at important and surprising data points, but it would take years for a scientist to scour every starscape image or spectroscopy. Machine learning classification algorithms allow us to fast-track this process by using already analyzed data to find anomalies.
One of the most interesting applications of this technology is to use image classification to detect celestial objects in images from the Hubble Telescope. The algorithm then uses past training data to determine if the object is a star, planet, nebula, or another type of object. It’ll then mark classifications that it’s uncertain about for scientists to review. This lets astronomers analyze images of space much faster and ensures they’re devoting their time to the most revealing findings.
Another prominent use of machine learning is to make predictions based on test data from a rocket’s internal sensors across multiple flights. This type of algorithm excels at finding trends undetectable to the human eye and allows engineers to find potential error sources or other throughlines to improve the design.
As a machine learning engineer, you’d focus on optimizing these algorithms to be as accurate and usable as possible. Every day, you’ll work with classification and detection algorithms to make sense of the near-infinite data found in space.
Continue learning about these skills on Educative
- Software Testing 101: Get started with software testing types
- What is Big Data? Characteristics, Types, and Technologies
- The top 10 ML algorithms for data science in 5 minutes
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