Have you ever relied on Google Maps to drive to a destination? Or watched a series recommended by Netflix? Or relied on Gmail’s smart compose to draft email replies faster? If you just nodded your head in agreement, then you too have witnessed the magic of machine learning firsthand.
1. Who is a Machine Learning Engineer?
The real-life examples we discussed above illustrate what a machine learning engineer can do. The main task of a machine learning engineer is to train and deploy machine learning models. The need for a machine learning engineer only cropped up a few years back. Prior to that, it was data scientists who built machine learning and statistical models.
However, data scientists would often build the solutions in things like Jupyter Notebooks, with zero business value. So, the need for a role that combines software engineering and design aspects arose. And this is where a machine learning engineer today sits. The job of a machine learning engineer is to bring algorithms to life so that they can generate tangible business value.
2. Why Hire a Machine Learning Engineer?
Let’s understand the value that a machine learning engineer brings to your business with an example.
John owns a Shopify store through which he sells t-shirts to Gen Z customers. The products have a customer connect and enjoy good brand recall. However, recently the business is struggling with customer churn. Despite strong traffic, many users browse products but don’t complete purchases.
John’s marketing team has tried attracting customers with generic campaigns, but they aren’t converting. Then, hearing a friend’s recommendation, John hired a machine learning engineer from Remote Resource. The AI specialist builds a recommendation engine that personalizes product suggestions based on user behavior, preferences, and past purchases.
The result? John’s store soon witnessed increased conversion rates, improved customer retention, and a measurable boost in revenue.
Like John, you can also hire a machine learning engineer from Remote Resource to build and deploy data-driven AI models into any work process. ML engineers possess theoretical knowledge of statistical analysis and the practical ability to create effective business application solutions using that knowledge. They use their knowledge to create applications that solve problems ranging from fraud detection to supply chain management, predictive diagnostics, and more.
3. Use case: How Netflix uses ML to deliver incredible user Experience
Netflix holds about 21% of the market share in the US among subscription-based video-on-demand services. Netflix maintains its market dominance by relying on machine learning to deliver an incredible user experience. One of the crucial applications of machine learning at Netflix is personalized recommendations.
US market share of Netflix
You must have personally seen this in action while using Netflix. Netflix has millions of subscribers and an extensive content library. Therefore, it is important for Netflix to show users relevant suggestions that are personalized according to their tastes. To make this happen, Netflix uses a technique called collaborative filtering. This method considers users’ viewing history, the behavior of similar users, browsing history, time of day, and more to offer accurate suggestions.
Netflix also uses ML for content categorization. It uses natural language processing to categorize its vast library of content. NLP analyzes the textual data associated with a title like summaries, reviews, and metadata to create micro genres and categorize content more effectively.
Subcategories in Netflix
Similarly, Netflix also relies on ML to personalize the artwork or thumbnail of a title and make them more appealing to a viewer.
Different thumbnails of a series based on differing user personas
4. What Skills and Technical Expertise Should a Machine Learning Engineer Posses?
A machine learning engineer requires a broad set of skill sets and is not an entry-level role. The potential candidate needs a few years of experience as a data scientist or as a software engineer before acquiring other skills to be eligible for a machine learning engineer role. A machine learning engineer should be well-versed in software engineering, machine learning, and data science.
They should have knowledge about software like Python, Docker, Git, SQL, AWS, Bash/Zsh, and more. The machine learning engineer should also have strong collaborative skills. It is also preferred that the candidate has a good understanding of decision trees, linear regression, and neural networks.
5. Should I Hire a Machine Learning Engineer Locally?
If you are looking to build AI models or set up a machine learning department, you might first think about hiring talents locally. However, you will likely encounter several hurdles while trying to execute such a move.
The Bureau of Labor Statistics predicts that the demand for machine learning engineers will grow by 23% between 2022 and 2032, significantly higher than other professions. However, compared to the demand, there is a lack of skilled professionals to meet it. For example, the UK alone needs 215,000 personnel who have hard data skills, but its universities together produce only 10,000 data science graduates.
This mismatch of supply and demand has jacked up the salaries of machine learning engineers. According to Glassdoor, the average salary of a data science engineer is around $130,000 per year. Companies also struggle to fill vacancies for lack of the right candidates with the necessary skills, leading to project delays and loss of competitive advantage.
6. Why Hire Virtual Employees from Remote Resource as ML Engineer?
Given the high demand and specific skill requirements, you will find it challenging to build ML solutions by solely relying on the regional marketplace for talents. You can, however, overcome this hurdle by sourcing candidates from areas beyond your regional marketplace and allowing the candidates to work remotely.
For instance, you can hire candidates from India, which has the second-largest AI/ML/Big Data Analytics talent pool. As of 2022, the country had an installed talent base of 416K professionals. However, you need not set up a virtual office in India to source and work with top AI specialists from India. You can just partner with Remote Resource and hire top talents at a fraction of local costs.
Final Words
If you are looking to deploy AI models to enhance customer experience or boost process efficiency, you need to hire a machine learning engineer. There is currently a heavy demand for these professionals, but the supply is limited. The skewed demand-supply situation has jacked up salaries and increased the time required to hire.
Businesses can, however, cross this hurdle by going beyond their regional marketplace in search of the right candidate. The easiest way to achieve this is by partnering with a remote staffing organization like Remote Resource.
But why should you believe us when you can experience it all with a one-week free trial? Yes, you read that right. We are so confident in our service offering that we are letting you try one week for free.
We won’t even ask for your credit card details.
And if you aren’t satisfied after a week, you can quit while retaining ownership of the work done during this period.
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