Jobsity’s Role Spotlight Series: Machine Learning Engineers

Welcome to Jobsity’s Role Spotlight Series, where we take you through the ins and outs of the most in-demand tech professionals. 

This week, we’re covering Machine Learning (ML) Engineers. What does their job look like? How does someone become an ML engineer in the first place? And are they the hire you need to make your project come to life? 

Let’s find out. 

A Machine Learning Engineer is a tech professional in a rising field. The combined demand for AI and ML specialists is expected to grow by 40% between 2023 and 2027. In fact, LinkedIn's “Jobs on the Rise” report highlights AI and ML as top skills driving job growth. With a 75% increase in postings over the past four years, it’s easy to see why. 

But what do they do?

Simply put, ML engineers specialize in designing, building, and deploying machine learning models and systems to solve complex problems. To do so, they need a blend of skills in computer science, mathematics, and statistics. This allows them to develop algorithms that can learn from data and use it to make predictions or decisions.

ML engineers most often work with large datasets. They use techniques such as data preprocessing, feature engineering, and model selection to draw conclusions and find insights. ML engineers must be proficient in programming languages, including Python or R. 

Machine learning engineers typically possess a strong foundation in computer science, mathematics, and statistics. The role requires a bachelor’s degree in one (most often computer science) or all of these disciplines. 

Many ML engineers also hold advanced degrees such as master's or Ph.D. in related fields. This provides them with the necessary theoretical background to understand complex algorithms and models.

However, don’t discount experience. While a theoretical background can be important, a candidate at the bachelor level with several years of experience is valuable. When it comes to machine learning positions, most fall into the category of “Bachelor’s required; Masters or equivalent experience preferred.” 

Equivalent experience to a Masters degree is most often cited as ten years or more in the field. 

While AI and machine learning are quickly becoming the norm across industries, let’s take a look at the top five use cases: 

1. Healthcare: In healthcare, machine learning engineers play a vital role in developing predictive models for disease diagnosis. They also support patient monitoring and the creation of personalized treatment plans. By analyzing vast amounts of medical data, the programs they create help healthcare professionals to make informed decisions. This ultimately improves patient outcomes.

2. Energy: In the energy sector, ML engineers optimize energy production and distribution processes. They develop algorithms to forecast energy demand, and improve efficiency in power generation. These algorithms can also identify potential equipment failures through predictive maintenance, reducing overall downtime and costs.

3. Manufacturing: Machine learning engineers allow manufacturers to streamline production and improve product quality. They analyze sensor data from manufacturing equipment, detect anomalies, and make real-time adjustments. This improves productivity and minimizes defects.

4. Food: In the food industry, ML engineers work across the board. They improve supply chain logistics and develop tracking processes to ensure food safety. By analyzing market data and trends, they also help companies tailor their products and strategies to meet evolving consumer demands.

5. Finance: In finance, machine learning engineers develop algorithms for fraud detection, risk assessment, and automated trading. The algorithms analyze data to allow institutions to identify suspicious activities, assess creditworthiness, and make sound investment decisions.

Naturally, it’s not only about what an ML engineer can do for your team, it’s about how they do it. When bringing a product to market or creating a new process, you need the best of the best—the top talent. And you need a team that’s going to collaborate effectively, a team who’s in it for the long haul. 

Our approach to staffing helps you level up your tech team, with long-term payoff. We find your ideal candidate, on your timeline, in your budget. We pride ourselves on taking the hassle out of hiring.

Jobsity devs stand head and shoulders above the rest, with an average retention rate of over 3 years. They represent the top 3% of LATAM talent, specializing in programming languages such as Python, R, and C++.  

That’s why companies like McGraw Hill and Creed Interactive trust Jobsity to provide the talent they need to make their projects a breeze.

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