What Does a Machine Learning Engineer Do? Roles, Skills, Salaries, and More

With the use of machine learning for automation, predictive analytics, and smarter decision-making, the demand for skilled professionals is growing. In fact, Machine Learning career opportunities will increase up to 200% in the coming decade.

So, what does a machine learning engineer do? 

They build models, fine-tune parameters, and integrate these systems into applications to make predictions. They collaborate with software engineers and data scientists to ensure the machine learning models meet business objectives.

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What Does a Machine Learning Engineer Do? Roles and Responsibilities

A machine learning engineer builds intelligent machines that can learn and improve from experience. Using concepts like deep learning and natural language processing, you can build machines that can automate tasks, such as customer response in e-commerce companies.

As industries like healthcare and finance are adopting machine learning for tasks like identifying fraud, skilled professionals are gaining demand, making it a lucrative career opportunity.

While the main duty of a machine learning engineer is to build intelligent machines, they perform functions like training the model and analyzing the performance.

Here are the core duties of a machine learning engineer.

1. Designing Machine Learning Models

Design algorithms and models suited to specific tasks, ensuring they can process data, make predictions, and improve over time.

Example: In healthcare, designing a deep learning model to detect anomalies in medical imaging data, like identifying early signs of tumors in X-rays.

2. Data Preprocessing and Feature Engineering

Feature engineering is the process of selecting the relevant input variables (features) that improve model performance.

Example: In the financial sector, cleaning up transaction records, normalizing values, and selecting relevant features to identify potential fraud patterns.

Also Read: Data Preprocessing in Machine Learning: 7 Key Steps to Follow, Strategies, & Applications

3. Model Training and Evaluation

Training models using labeled data and evaluating their performance using various metrics to ensure accuracy and reliability.

Example: In e-commerce, train a recommendation system model that predicts products customers are likely to purchase next and also check performance using metrics such as precision, recall, and F1 score.

Also Read: Evaluation Metrics in Machine Learning: Top 10 Metrics You Should Know

4. Model Optimization 

Adjusting model parameters, applying optimization techniques, and tuning hyperparameters to achieve better results.

Example: In autonomous driving, optimizing a deep learning model responsible for object detection, ensuring it accurately identifies obstacles.

Also Read: Types of Optimizers in Deep Learning: Best Optimizers for Neural Networks in 2025

5. Model Deployment and Monitoring

Once models are trained and evaluated, they need to be deployed to production systems and continuously monitored for performance degradation.

Example: In a retail setting, you will monitor the model’s accuracy, retrain it periodically, and address any performance concerns that may arise, such as an unexpected shift in customer behavior.

6. Collaborating with Cross-Functional Teams

Machine learning engineers need to work with data scientists, software engineers, and stakeholders to align machine learning models with business objectives.

Example: In the manufacturing industry, collaborating with engineers and production managers to implement predictive maintenance models that can predict equipment failures. 

7. Ensuring Data Privacy and Security

Ensure that sensitive data is securely handled in compliance with regulations like GDPR or HIPAA.

Example: While working with healthcare data, you must ensure that patient information is anonymized and stored securely to comply with privacy regulations. 

While the responsibilities mentioned above are general for machine learning engineers, they can vary based on the engineer's experience level. Let’s explore how these responsibilities change as one gains more experience.

Key Roles and Responsibilities of a Machine Learning Engineer

Entry-level machine learning engineers mainly handle tasks like data collection and cleaning. With experience, you will perform advanced tasks like designing machine learning models and optimizing performance.

Here are the roles and responsibilities of machine learning engineers based on experience.

Now that you’ve seen what a machine learning engineer does, let’s understand the skills needed for this role.

Key Skills Every Machine Learning Engineer Should Learn

As a machine learning engineer, you need both technical skills, such as data preprocessing, and workplace skills, like effective communication, to succeed in your role. 

These skills enable you to build, optimize, and deploy machine learning models efficiently while collaborating effectively within teams.

Here are the key machine learning engineer skills.

Also Read: Top 30 Machine Learning Skills for ML Engineers in 2024

Now that you’ve explored the technical and workplace machine learning engineer skills, let's take a look at how you can become one.

How to Become a Machine Learning Engineer: A Step-by-Step Roadmap

To become a machine learning engineer, you need a combination of education, technical skills, and hands-on experience in AI and data analytics. In addition, a project portfolio and internship experience can help you secure a job in this field.

Here are the steps involved in becoming a machine learning engineer.

Step 1: Earn a Degree or Credential

A bachelor's degree in software engineering, data science, and computer engineering is often required. Alternatively, you can pursue specialized machine learning certifications.

Example: upGrad’s Executive Diploma in Machine Learning and AI course can help you master machine learning.

Step 2: Improve Your Skills

Focus on increasing knowledge of programming languages such as Python (widely used for ML tasks), R, and Java. Learn to use popular machine learning frameworks like Keras, TensorFlow, and PyTorch to build and deploy models.

Mathematical concepts like linear algebra, calculus, and probability will also be beneficial for machine learning applications.

Example: Build a recommendation system using Python and TensorFlow for an e-commerce platform to refine both coding and model-building skills.

Learn basic Python programming to build a strong foundation for machine learning. Join a free course on Learn Basic Python Programming and prepare for a career in machine learning.

Step 3: Build a Portfolio

Create machine learning projects that solve real-world problems. Build projects like a sentiment analysis tool using natural language processing (NLP) for social media data. Upload your work to GitHub and show it to your potential employers. 

Example: If you’re interested in healthcare, create a project that uses machine learning to predict patient outcomes based on medical data.

Step 4: Gain Experience

Look for internships or junior machine learning engineer roles that can help you contribute to real-world applications. By working on practical projects, you can understand the challenges involved in preparing data, building models, deployment, and performance evaluation.

Example: Internships in companies like IBM or Google will give you practical exposure to the tools and technologies used in large-scale machine learning applications.

Step 5: Apply for Jobs

Once you've gained enough practical experience and have a solid portfolio, start applying for machine learning engineer roles. 

Research companies that are using machine learning for data-driven solutions. Prepare for technical interviews that may include coding challenges and algorithm design questions.

Example: If you’re applying for a role at an automotive company focused on autonomous driving, prepare for questions about computer vision and reinforcement learning.

Now that you know how to begin your career as a machine learning engineer, let’s focus on the job roles and career progression within this profession.

Job Growth and Career Outlook for Machine Learning Engineer

Roles like AI/ML specialist and machine learning architect are increasingly in demand. With leadership qualities, you can progress to higher positions such as ML Engineering Manager or Chief Data Scientist.

Here are the future career opportunities for machine learning engineers.

Source: Glassdoor, Ambitionbox

Now that you’ve looked at how salary varies based on career opportunities for machine learning engineers, let’s understand how you can build a career in this field.

How Can upGrad Help You Become a Machine Learning Engineer

A career in machine learning requires skills such as programming, proficiency in frameworks, and a strong understanding of mathematical concepts like linear regression. Internships and hands-on projects, particularly in building predictive models, can give you a competitive edge.

To develop these skills, upGrad’s courses can be your reliable partner. These courses help build your foundational knowledge while offering practical experience through real-world projects, ensuring you’re well-prepared for the industry.

Here are some courses offered by upGrad in machine learning.

• Post Graduate Certificate in Machine Learning and Deep Learning (Executive)
• Post Graduate Certificate in Machine Learning & NLP (Executive)
• Fundamentals of Deep Learning and Neural Networks
• Linear Regression - Step-by-Step Guide
• Logistic Regression for Beginners

Do you need help deciding which courses can help you in machine learning? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.

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Reference Links:

https://www.glassdoor.co.in/Salaries/data-scientist-salary-SRCH_KO0,14.htm
https://www.ambitionbox.com/profile/machine-learning-specialist-salary
https://www.glassdoor.co.in/Salaries/machine-learning-architect-salary-SRCH_KO0,26.htm
https://www.glassdoor.co.in/Salaries/machine-learning-manager-salary-SRCH_KO0,24.htm
https://www.glassdoor.co.in/Salaries/chief-data-scientist-salary-SRCH_KO0,20.htm
https://www.turing.com/kb/machine-learning-career-guide-and-in-demand-ml-job-roles