How to Become a Machine Learning Engineer in 2024

With the demand for machine learning (ML) engineers continuously rising, many job seekers wonder how to become one. According to the U.S. Bureau of Labor Statistics, the machine learning engineering field is predicted to grow by 23% from 2022 to 2032. The largest market is projected to be in the United States, with an expected value of US$21.14 billion this year.

There are multiple facets to becoming a machine learning engineer and staying on top of the game, from having the necessary skills and educational background to earning the needed certifications and practicing interviews.

But before we dive deep into how to become a machine learning engineer, let’s first cover the basics.

Machine learning engineering is a transformative force that focuses on creating and implementing algorithms that enable machines to learn and make database decisions. A machine learning engineer is the one who designs and implements these algorithms to solve problems, enhance processes, or create optimized models.

There are different skills involved in becoming a machine learning engineer, especially since such work involves developing intelligent systems that automatically and optimally learn and improve from experience. These skills can be found and determined when you apply as a machine learning engineer.

For example, if you want to become a machine learning engineer for Netflix, you will need the essential skills to build machine learning frameworks to keep Netflix’s audiences continually engaged with the product.

As you look across job postings for a machine learning engineer, you can find the following qualifications:

• Design and implement scalable machine learning models using suitable algorithms for different tasks;
• Clean data and address inconsistencies;
• Leverage statistical analysis to improve the quality of machine learning models;
• Manage the infrastructure required to take a model to production;
• Build data and model pipelines;
• Monitor machine learning systems in production and retrain them as necessary;
• Deploy machine learning models; and
• Build machine learning frameworks.

When you become a machine learning engineer, you become an essential part of the company that transforms theoretical data science models into practical applications that automate tasks, enhance decision-making, and unlock new data-driven insights. Moreover, you enable organizations or companies to leverage advanced analytics and AI-driving innovation and efficiency in present times.

A couple of factors make becoming a machine learning engineer in demand nowadays. Here are some reasons why:

1. Increase in Data - To process and extract valuable insights with the increase in data;
2. Task Automation - To improve efficiency and reduce costs, companies are automating repetitive tasks with the help of AI;
3. Advanced Analytics - To help businesses make data-driven decisions; and
4. Personalized Customer Experiences - To create personalized customer experiences that boost satisfaction and loyalty, especially in retail and marketing.

Despite the ongoing and increasing demand for ML engineers, there is still a significant talent gap between applicants, resulting in companies struggling to find qualified candidates, which creates a competitive advantage for engineers with the right skills. This translates to advantageous salaries, with ML engineers having one of the highest wages in the tech sector.

However, it is important to take note that machine learning engineering is a specialized role that requires a unique combination of different skills, such as statistical analysis, data science, and software engineering. While the job description typically requires an educational background in engineering or computer science, the tech sector emphasizes specific skills and capabilities necessary for the role. This suggests that a machine learning engineer degree is not strictly required to meet the job’s requirements.

Educational Background

A higher education translates to greater value in the machine learning engineering job market, while a master’s or PhD can further boost your chances. However, if you are coming from a different sector and want to dive into machine learning engineering, there’s no need to fret. The tech industry focuses on what you can bring to the table and not the degree you hold. Despite your degree, you can ease into the machine learning engineering job market with the right combination of skills, portfolio, learning, and determination.

Technical Skills

Machine learning engineers need a comprehensive set of technical skills to ensure that intelligent systems are effectively optimized. You can use these various technical skills to create, train, and deploy efficient, scalable ML models.

Here are some technical skills you need to possess when becoming an ML engineer:

Programming Skills

There are various programming languages available, but certain programming skills are required for a career in ML to code algorithms, manage data, and implement models that enable machines to learn and make decisions. We have highlighted these top programming skills below:

Python

Python dominates this list due to its robust libraries for deep learning, machine learning, and data analysis, such as TensorFlow and scikit-learn, which dominate the ML landscape. This programming language enables the development of sophisticated algorithms and the processing of large datasets, which makes it an essential requirement for machine learning engineers.

SQL

Learning SQL is indispensable due to its capacity for managing and querying data stored in relational databases, which makes it crucial for data preparation and feature engineering in ML workflows. Some employers ask SQL questions during interviews to gauge your knowledge about this program.

Java

Java is another programming language that is highly crucial for environments with large-scale data processing frameworks like Apache Hadoop and Apache Spark.

Meanwhile, there are other helpful programming skills to have if you want a career as a machine learning engineer, such as R, another programming language favored for statistical analysis, and Javascript, which is ideal for integrating ML models into web environments.

AI Skills and Tools

Machine learning is a subset of AI that involves algorithms learning from data to make predictions or decisions without being explicitly programmed. As such, AI skills are integral to the role of an ML engineer.

Machine learning is a crucial skill for ML engineers, as mentioned in most job postings. The most in-demand ML fundamentals employers look into are deep learning, natural language processing (NLP), computer vision, optimization, artificial neural networks (ANN), and reinforcement learning. For example, deep learning uses ANN to mimic the process by which humans learn tasks and solve problems. Meanwhile, NLP enables computers to understand and communicate with human language which is usually paired with another critical AI skill required for the job, such as the knowledge of large language models (LLMs) that can comprehend and generate human language text.

Moreover, ML engineers utilize various specialized frameworks and libraries to work within these subfields of AI. These specialized tools are critical requirements for ML engineer job seekers and offer different functionalities from deep learning and neural network development to more traditional machine learning processes.

There are also job postings that require ML engineering job seekers to have skills in feature engineering, predictive modeling, and Bayesian methods, as well as specialized machine learning techniques such as anomaly detection and clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Bayesian methods, for example, assist algorithms in extracting crucial information from small data sets and in handling missing data, while RNN is used to process and convert sequential data (words, sentences, or time series) input into specific sequential data output.

Cloud Skills

Different cloud platforms provide a myriad of services and tools that allow ML engineers to efficiently bring ML solutions to the market. Cloud skills are indispensable when becoming an ML engineer. They allow ML professionals to leverage scalable computing resources to handle the intensive computational demands of training and deploying machine learning models.

The top platforms for cloud skills include Microsoft Azure, AWS, Docker, Kubernetes, Google Cloud, and Salesforce.

Data Engineering, Architecture, and Analysis Skills

Aside from traditional ML engineering skills, employers are seeking engineers with multifaceted skill sets, particularly in small companies with limited resources and a small team handling all data-related tasks.

Meanwhile, employers are also looking for ML engineers with data mining and data visualization as part of their skill sets. They look for applicants who have experience with data collection, cleaning, and processing, as the job often requires working with messy data that needs to be transformed.

Soft Skills

Aside from technical skills, employers will also look at your soft skills, including problem-solving, communication, and collaboration, which are essential for transforming a capable team into a successful one. Soft skills are crucial in the field as they enable you to work effectively with clients, stakeholders, and team members to resolve issues and deploy the right solutions. Technical and soft skills go hand-in-hand to make you and your team strong, capable, and successful.

Starting a career in ML engineering can be a daunting task, especially if your degree is in a different field. It’s better to equip yourself with the right tools and mindset to navigate and jumpstart your career in ML engineering.

We have provided below what you can do to ensure you’re right on the right track:

1. Education, Training, and Practical Experience

The AI and ML fields are rapidly evolving, so you must stay updated and informed by reading research papers, attending conferences, earning certifications, and participating in workshops or webinars.

2. Work on Your Skills

Be involved in projects that showcase and build on your skills, especially those related to ML engineering, which can help you build a strong portfolio that shows your abilities and potential to future employers.

3. Learn the Interview Questions for the Company You’re Applying For

In addition to technical and soft skills, practice interview questions involving machine learning engineering to ace the interview. Your skills and knowledge may get you through the first round, but it’s in the interview that your employer gauges whether you’re a fit for the team. If you’re planning to apply to a specific company, interview questions like those from Interview Query can help you ace the interview process.

4. Build Connections

You can join professional networks and AI communities for valuable insights, opportunities, and collaborative efforts. Put effort into attending meetups, joining groups, or participating in online forums to build your connections and future references in the field.

If you’re looking for a career with stability, growth potential, and intellectual challenge, then pursuing ML is an excellent choice. With the expected increase in the market volume of AI and ML, there’s no question that being an ML engineer is one of the top choices today.

However, with the increase in market volume comes the demand for more ML engineers. This is why you must stay on top of your game to get hired or start your ML engineering career. Aside from fulfilling all the skills and education required, be prepared for company interviews to boost your chances of getting picked. Gain the edge over your competitors now through Interview Query’s vast range of resources for your job interview.