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? 

A machine learning engineer is responsible for designing, building, and deploying machine learning models that help businesses make data-driven decisions. They bridge the gap between data science and software engineering, ensuring that predictive models are scalable, efficient, and accurate.  

Daily Responsibilities of a Machine Learning Engineer 

• Collecting and preprocessing large datasets for model training 
• Designing and developing machine learning models using Python, TensorFlow, or PyTorch 
• Evaluating model performance and tuning hyperparameters 
• Collaborating with data scientists and software engineers to integrate models into applications 
• Monitoring and maintaining deployed models in production environments 
• Staying updated with emerging tools and techniques in AI and machine learning 

Key Skills Required to Become a Machine Learning Engineer 

Becoming a successful machine learning engineer requires a combination of technical expertise, analytical abilities, and practical experience. Knowing the right machine learning skills, machine learning engineer skills, and skills needed for machine learning is essential to thrive in this high-demand role.

Core Technical Skills: 

• Programming: Proficiency in Python, R, or Java for implementing ML algorithms 
• Machine Learning Algorithms: Understanding supervised, unsupervised, and reinforcement learning 
• Data Handling & Preprocessing: Cleaning, transforming, and managing large datasets 
• Model Development & Evaluation: Designing, training, and testing models using frameworks like TensorFlow or PyTorch 
• Feature Engineering: Creating meaningful features to improve model performance 

Analytical & Problem-Solving Skills: 

• Strong ability to interpret data and extract actionable insights 
• Critical thinking to optimize models and troubleshoot errors 

Additional Skills: 

• Knowledge of machine learning skills required for building scalable pipelines 
• Familiarity with cloud platforms and deployment processes 
• Soft skills such as communication, teamwork, and project management 

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 PyTorchto 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

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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