How to Become a Machine Learning Engineer

by Danielle Antosz | Published on March 12, 2025

If you've ever used Siri on your phone or received automatic fraud detection alerts on your credit card, you've seen machine learning in action. The people behind these intelligent systems are called machine learning engineers. 

Machine learning engineers work with massive data sets and complex algorithms to create cutting-edge smart systems that can automate tasks, detect complex patterns, and even learn how to improve over time. Unlike traditional software engineers, who write specific rules for computers to follow, machine learning engineers teach machines to learn on their own. 

This guide explains exactly what this role entails, what salary you might earn, career prospects, and the technical skills you'll need. 

What is a Machine Learning Engineer?

Machine learning engineers build the models that power AI-driven applications. Unlike data scientists, who analyze data to extract insights, machine learning engineers focus on designing, developing, and deploying machine learning models. Their role is a mix of software engineering, data science, and artificial intelligence, making it one of the most in-demand jobs in tech today.

Think of a machine learning engineer like a genius chef who creates a self-improving recipe. Instead of cooking the dish once, they design a recipe that learns and improves each time it's made. The chef gathers ingredients (data), tests different cooking methods (algorithms), and adjusts seasonings (model parameters) to make sure the final dish (AI system) gets better with every iteration. Their job is to ensure the model doesn’t just work once—but continues to refine itself based on new data, just like a great chef perfecting a signature dish over time.

What Does a Machine Learning Engineer Do?

Machine learning engineers are in the weeds of developing machine learning models. They're a critical part of the AI revolution, working with cutting-edge technology to develop smart systems that help make computer systems smarter. Their key responsibilities include:

• Designing and developing machine learning models: Build and refine algorithms for predictive analysis, automation, and AI-driven applications.

• Data preprocessing and feature engineering: Clean, transform, and optimize datasets to ensure models receive high-quality, structured data.

• Training and testing models: Experiment with different algorithms, hyperparameters, and architectures to improve accuracy and efficiency.

• Deploying machine learning models: Integrate models into production systems, cloud environments, and real-time applications.

• Monitoring and optimizing models: Track model performance over time, retrain as needed, and adapt models to new data and business needs.

Machine learning engineers bridge the gap between AI research and real-world applications, making sure models don’t just work in theory—but actually deliver value in production. 

A Typical Work Day for a Machine Learning Engineer

The actual tasks can vary based on where you are in a project, the tools you use, and your industry. But, a typical day might look something like this: 

• Morning: Review model performance, debug errors, and preprocess new datasets.

• Midday: Meet with data scientists, engineers, and business teams to discuss AI initiatives and model deployment.

• Afternoon: Write and optimize Python or R scripts to train and test new models, tune hyperparameters, and experiment with deep learning architectures.

• End of day: Deploy models to cloud environments like AWS SageMaker or Google Cloud AI, document findings, and plan for the next iteration. 

What Skills Do Machine Learning Engineers Need? 

To succeed as a machine learning engineer, you’ll need a mix of programming, data handling, and AI expertise. Here are the core skills:

• Programming languages: Proficiency in Python or R (with experience in frameworks like TensorFlow, PyTorch, and Scikit-learn).

• Data processing and analysis: Strong knowledge of Pandas, NumPy, and SQL for working with large datasets.

• Machine learning algorithms: Understanding of supervised, unsupervised, and reinforcement learning models.

• Statistics and mathematics: Expertise in linear algebra, probability, calculus, and optimization to build effective models.

• Cloud computing: Experience with AWS, Google Cloud, or Azure to train and deploy models at scale.

• Big data technologies: Familiarity with Hadoop, Spark, and distributed computing for handling large datasets.

Machine learning engineers also need soft skills, like collaboration, problem solving, and the ability to stay organized and on task. 

Education and Training Resources

Machine learning engineers typically have a strong academic background in computer science, math, or data science, along with hands-on experience working with AI models. 

Most hold a bachelor’s degree in computer science, mathematics, or a related field. Many also pursue advanced degrees, such as a master’s or Ph.D. in artificial intelligence, machine learning, or data science, to gain deeper expertise.

Certifications for Machine Learning Engineers

College degrees alone might not be enough in this field. Certifications help validate expertise and improve job prospects. Some of the top machine learning certifications include:

• Google Cloud Professional Machine Learning Engineer: Covers ML model deployment, data pipelines, and cloud-based AI solutions.

• AWS Certified Machine Learning – Specialty: Focuses on ML development and deployment using AWS services.

• Microsoft Certified: Azure AI Engineer Associate: Validates expertise in Azure Machine Learning, AI workloads, and automation.

Earning certifications can fast-track career progression and show potential employers you have the skills to succeed.

Machine Learning Engineer Career Path and Progression

Machine learning engineers build experience over time, often several years. Many begin their careers as:

• Junior machine learning engineers: Assist in model development and data processing.

• Data analysts: Work with datasets to extract insights and prepare data for machine learning models.

• Software engineers: Develop software that integrates AI and machine learning algorithms.

With experience, they may move up to senior roles, like senior machine learning engineer, team lead, or machine learning architect. Some may choose to focus on AI specifically or move into related fields like data science or big data engineering. 

How Much Does a Machine Learning Engineer Make?

Machine learning engineers earn competitive salaries, reflecting their specialized skill set. Here’s what you can expect to earn based on experience level:

Source: Glassdoor, as of December 2024

With AI-driven innovation expanding, machine learning engineers can expect strong salary potential and job stability. However, several factors can impact the salary you'll earn, including: 

• Industry: Tech, finance, and healthcare companies pay the most for machine learning talent.

• Location: Expect higher salaries in Silicon Valley, New York, and major AI research hubs.

• Experience & certifications: Advanced skills and certifications can boost earning potential.

The Path to Your New Machine Learning Career Starts Here 

Becoming a machine learning engineer takes time, education, and experience, but it's an exciting and rewarding career with high earning potential.

Get started by learning Python, experimenting with AI models, earning certifications, and applying for entry-level machine learning roles. As AI continues to shape the future, there's never been a better time to break into this field!

Ready to get started? Claim your free week of training at CBT Nuggets.