What Does a Machine Learning Engineer Do? Roles Explained

Machine Learning (ML) Engineers are an integral part of this technology-driven era, in which almost all industries are increasingly adopting AI/ML. They help design and deploy AI models for use by organizations. In this blog, we will discuss what a Machine Learning Engineer does and how they benefit the community.

It will be your guide to better understanding their roles and responsibilities, the skills required for a successful career, and the various pathways available after completing the relevant degree. We also tell you how upGrad collaborates with reputed universities to help you upskill for the role and guide your future career trajectory.

What Does a Machine Learning Engineer Do Day-to-Day?

In this section, we discuss what a Machine Learning Engineer does in their everyday life. To start with, it helps to have a fair understanding of data science and software engineering. There are various types of Machine Learning Engineers, depending on expertise and skills. Their job responsibilities also vary by role and seniority level.

Building and Training Machine Learning Models

Building and training machine learning models is one of the basic responsibilities of an ML Engineer. They are responsible for developing models that produce accurate, efficient results. Some of their key dealings involve:

• Collecting datasets to understand patterns
• Selecting algorithms in applications
• Evaluating the efficiency of different AI systems

Deploying Models into Production

After designing and building a successful AI model, ML Engineers must deploy it efficiently in production. They are required to transfer the models from the development to the live environment. Basic responsibilities at this stage include:

• Using cloud storage to handle the data
• Monitoring performance
• Handling any issues

Collaborating With Cross-Functional Teams

ML Engineers must also collaborate with different departments, for which they must have a good understanding of their responsibilities. Good communication skills and teamwork capabilities play a key role here. They collaborate with the following teams daily:

• Software Engineers
• Data Scientists
• Other Stakeholders

Key Skills Required to Become a Machine Learning Engineer in the USA

A Machine Learning Engineer is required to possess technical skills for building efficient and robust AI models, as well as strong soft skills to communicate effectively with different departments. 

Technical Skills You Need

ML Engineers should have a good understanding of programming languages to design and build AI models. There are numerous online courses, certification programs, and bootcamps available to equip these skills. Some of the commonly used programming languages by ML Engineers today are:

• Python
• Java
• R

Data and Analytical Skills

ML Engineers should also have strong data analysis skills to handle large, complex datasets. They should be able to extract insights from data that facilitate efficient decision-making and management. A good understanding of the following helps in the process:

• Statistics
• Probability
• Data Cleaning

Soft Skills That Matter

As ML Engineers work with different teams, they also need certain soft skills to succeed in their roles. They should be able to explain complex technical concepts in simple terms to stakeholders. Some of the essential soft skills crucial here are:

• Critical Thinking
• Teamwork
• Effective Written and Verbal Communication

Also Read: How to Build a Generative AI Portfolio That Attracts US Employers

Career Paths and Opportunities in the USA

After completing a relevant degree in ML Engineering, graduates can explore different career paths. Their job role would largely depend on experience and skills. In this section, we discuss some key job roles, along with their average annual salaries, to present a clearer picture: 

1. Artificial Intelligence Engineer: AI Engineers help build AI-powered applications. Their annual salary ranges from USD 87,428 to 141,690.
2. Machine Learning Engineer: They design and build machine learning models across different applications. Their annual salary typically ranges from USD 94,878 to 154,371.
3. Data Scientist: They track datasets to identify patterns and trends. Average annual salary ranges from USD 92,672 to 149,799.

Source: Salary Expert, as of May 2026

Also Read: Machine Learning Careers in the USA Supply Chain and Logistics Industry

Start Your Machine Learning Career with upGrad

If you’re interested in building a career in Machine Learning, upGrad can help. The platform offers several relevant courses in the field, in collaboration with reputable universities globally, to help beginners get started and experienced professionals upskill for senior roles in the evolving ML domain. Some of the popular Machine Learning courses offered on upGrad are:

1. Master of Science in Machine Learning and AI from Liverpool John Moores University: This WES-recognized course can be completed in 18 months. The benefits of pursuing this course are:

• Option to attend immersion at the UK campus
• Access to real-world case studies
• Experience of working with more than 80 programming tools

2. Executive Diploma in Machine Learning and AI with IIIT-B: This WES-recognized course can be completed in 12 months. The top features of the program are:

• Option to attend a 3-month free programming bootcamp
• A host of programming languages
• Option to choose from 2 specialization tracks

FAQs On Machine Learning Engineers’ Roles