Completing your study as a machine learning engineer is something to be proud of. But after graduating, what happens next?
Of course, you’ll want to join the workforce as soon as possible and preferably engage in an ML engineer job.
Machine learning engineers are core members of any data science organization. These highly proficient programmers are involved in the research, building, and design of artificial intelligence systems, which are the main components of today’s machine learning.
ML engineer careers also involve the rigorous maintenance of existing AI systems and the application of adjustments and improvements as needed. The ML engineer would frequently need to communicate with other data science teams who are working on a new system, so they should be conversant in the language of this industry.
While there might be some slight differences in what a machine learning engineer does, it includes the following responsibilities:
- Execute machine learning system algorithms
- Analyze data and transpose it into working data science prototypes
- Continuously test the AI systems and conduct tests
- Work hand-in-hand with the development teams of machine learning systems
- Maintain the current AI systems
- Upgrade and optimize the plans as needed
- Visualize and perform data analysis
- Predict possible results based on data
- Provide training and retraining as necessary for the ML systems, prototypes, and models
- Interpret data that can impact business decisions
Being an ML engineer is challenging because it entails a complete understanding of the basics of computer science, data analysis, and data processing. Aside from that, you also need to have the soft skills acquired from these disciplines, aside from the ability to work in a team.
When you already have experience working as a Machine Learning engineer, you’ll have an idea of what the job entails. Still, for those newly joining the workforce, here are the essential skills you need to succeed and kick-start your career.
Everything about algorithms is fundamental in all ML engineer jobs. It includes knowing how to write, understand, and read the processes, comprehending data structures, and knowing the basics of computer architecture.
The expected output of an ML engineer comes in the form of software, so the proper software practices, version controls, and testing is basic knowledge.
One of the expectations of employers from job seekers wanting to land ML engineering jobs is competency in data modeling, deep learning, computer vision, and proficiency in today’s most in-demand high-level programming languages, specifically Python, R, and Java.
A good grasp, understanding, and interpretation of statistics and probability is also a significant plus.
Math and mathematical operations are all part of the day-to-day life of a machine learning engineer. So you need to have a great interest in algebra, linear equations, and statistics to succeed in this field.
So you’re looking for ML Engineering jobs?
Our AI-powered platform is designed to help you stand out and get noticed by top employers worldwide. Why not sign up for a free account?
Possible Machine Learning Engineer Jobs
There are plenty of career opportunities that you can land. The good news is ML engineering jobs are overflowing, and there’s no stopping it.
The possible career paths that ML engineer graduates can explore are:
Software Engineer Or Software Developer
The main task of a software developer’s job title is to create and design different systems that can solve real-world problems and automate daily processes to make them more accurate and easy to handle.
This career entails continuous data analysis and testing to achieve actionable insights to make decisions. Data scientists typically go into root-cause analysis and cause-and-effect systems to enhance processes and strengthen the organization. Determining variables and data sets and the proper way to use this information is one strength of a data scientist.
Data engineers work closely with data scientists. Their tasks include transposing and interpreting data and converting it into understandable language and actionable presentations. Data engineers also play a primary role in designing, building, testing, optimizing, and managing data and systems.
How High Is The Demand For ML Engineer Jobs?
With the world moving towards more powerful and innovative technology, more companies have become desperate to hire machine learning engineers.
There is high demand for machine learning engineer jobs, but minimal resources exist. The complexity of machine learning, its models, and systems requires continuous understanding and an above-average skill set in software engineering and data analytics. As of now, the pool of machine learning engineers is still limited.
But the demand will stay strong for a while, and the need will continue to spike. This is because even if systems and codes now drive most processes, ML engineers are the people behind them. Their fine-tuning skills, a keen eye for details, and problem-solving skills will always be needed.
The significant shortage of this talent is not just apparent in specific cities in the US; the deficit is worldwide on a larger scale.
How Much Does a Machine Learning Engineer Jobs Pay?
Because of the specificity of the knowledge and experience, ML professional jobs are among the best-paid careers in the workforce. And it is only fair given that the job requires specialists with competitive knowledge, suitable skill sets, and the adeptness to work in the ever-changing field of science and technology.
Machine learning engineers receive a higher salary than the national average, with most starting jobs offering six-figure compensation. Of course, the more experience you have, the more you can earn.
As of June 2022, the average base salary for an ML engineer is $120,311, with New York City and San Francisco, California, pegged as the cities that pay the most.
Indeed, ML engineer jobs are one of the most lucrative careers today, but they also demand a higher level of competency and skill. It is a rewarding job, especially if you thrive on continuous challenges and have a unique passion for data and algorithms. As long as you have the necessary software engineering, data, and mathematical skills and the willingness to keep up with the latest developments, you will be able to land a suitable job.
The Shortest Path to Top Talent
Talentprise is a Skill-driven, AI-powered talent sourcing platform. Helping job seekers land the Tech jobs they love and deserve, while enabling companies to find the best-fit candidates for their business.
If you are a technology professional who’s looking to find dream jobs in any part of the world, sign up for your free account today.
If you are seeking employment in ML or deep learning engineering, sign up today and select Data Scientists as your occupation.
You may review the entire occupation list to check which occupations are suitable for your skills and expertise.
Keren Dinkin had seven years of work experience in digital marketing and e-commerce before shifting gears and becoming a full-time content writer. She loves words and the power they hold, which explains her passion for writing.
Keren is based in California. When she is not at her desk whipping up compelling narratives and sipping on endless cups of coffee, you can find her curled up with a book, playing with her dog, or pottering about in the garden.