Machine Learning Engineer

How to Become a Machine Learning Engineer

As companies integrate artificial intelligence into products and business operations, they need engineers who can turn machine learning (ML) models into dependable, scalable applications. Machine learning engineers combine expertise in AI with the software engineering skills required to build, deploy and maintain ML systems. 

Learning how to become a machine learning engineer starts with understanding the technical knowledge and experience the role requires. The path typically includes a foundation in computer science and mathematics, proficiency in programming, specialized training in machine learning and hands-on experience creating and deploying models. 

This article covers the steps and skills that can prepare you to pursue a career in machine learning engineering.

What Does a Machine Learning Engineer Do?

Machine learning engineers incorporate trained ML models into software for consumer products and business applications. They build the technical infrastructure needed to support these applications and keep them running reliably and efficiently. Common responsibilities for ML engineers include: 

• Developing and refining models: Machine learning engineers train models using large datasets and test how accurately they perform a given task, such as recognizing objects in images or detecting fraudulent transactions. They may compare algorithms, adjust model parameters and make changes to improve accuracy, speed or computational efficiency. 
• Preparing and managing data: ML models depend on data that’s accurate, consistent and structured appropriately for the task. Engineers create processes for collecting, cleaning, transforming and validating that data before it’s used for training or predictions. 
• Building ML pipelines: Engineers create automated workflows for the stages involved in developing and operating a model, from data preparation and training through validation and deployment. These pipelines make it possible to repeat those steps efficiently when new data becomes available or a model needs to be retrained. 
• Deploying models: Once a model has been trained and tested, engineers integrate it with the software it’s designed to support. This may involve creating APIs, configuring cloud infrastructure and making sure the model can respond quickly and reliably to requests from an application. 
• Monitoring performance: Engineers track measures such as model accuracy, response time, resource usage and errors after deployment. They also watch for changes in incoming data that can reduce a model’s accuracy, then retrain or update it when needed.

How to Become a Machine Learning Engineer: 5 Steps

There’s no single path to a career in machine learning engineering, but the role requires preparation across several technical disciplines. Below are some common steps for building the knowledge and experience typically needed to work in this field.

1. Establish Your Technical Foundation

A bachelor’s degree in computer science, engineering, mathematics, statistics or another quantitative field is a common starting point for aspiring ML engineers. These programs can provide a solid foundation in computational thinking and key mathematical concepts such as linear algebra, calculus, probability and statistics. You’ll draw on these subjects when developing algorithms, training models, interpreting results and diagnosing performance issues. 

Professionals moving into ML from another field can complete additional coursework as needed to bridge any gaps in their math and computer science background.

2. Build Your Programming and Software Engineering Skills

Python skills are essential for most machine learning engineering roles. Proficiency in writing and debugging Python code, along with knowledge of data structures and algorithms, helps engineers process data and run ML workloads efficiently. 

Core software engineering skills are also needed to build ML models into larger applications. This includes using version control, writing tests, organizing code and collaborating with other developers within a shared codebase. These skills help ensure ML components can be maintained, updated and integrated with other software.

3. Develop Specialized Machine Learning Expertise

After developing fundamental programming and math skills, aspiring ML engineers move into specialized study of machine learning, covering topics including model training and evaluation, neural networks and deep learning. Students often study these topics in upper-level undergraduate courses or graduate programs, using frameworks such as PyTorch to build and train models in code. 

A graduate program such as an M.S. in Artificial Intelligence can provide a structured path to advanced training in machine learning and related AI fields.

4. Learn to Build and Deploy Production ML 

ML engineers need experience moving trained models from development into production environments, where they can be used within an application. This includes learning how to deploy models on cloud infrastructure, build automated pipelines for updating and retraining them, and monitor performance after release. Engineers can develop these production-focused skills through training in MLOps, a set of practices and tools for deploying, monitoring, maintaining, and updating ML models in production. 

5. Apply Your Skills Through Projects and Professional Experience 

Early-career ML engineers often build portfolios of their work, such as recommendation systems, image classification models or NLP applications, with documentation showing how their models were developed and evaluated. Internships can give students and recent graduates valuable experience working with larger datasets, shared codebases and development teams. Hands-on research projects and open-source contributions offer additional ways to apply ML engineering skills and demonstrate technical experience.

What Skills Do Machine Learning Engineers Need?

Machine learning engineering draws on several areas of technical expertise, from programming and mathematics to model development and deployment. Understanding common machine learning engineer skills can help early-career professionals and career-switchers assess their existing knowledge and identify areas where additional training may be needed. 

Programming and Software Engineering

Python is widely used for machine learning development, supported by libraries and frameworks such as NumPy, pandas, scikit-learn and PyTorch. ML engineers also benefit from experience with data structures, algorithms, APIs, version control, testing and other software engineering practices used to build and maintain production applications. 

Mathematics and Statistics

Linear algebra, calculus, probability and statistics provide the mathematical foundation for understanding how ML algorithms learn from data. These subjects are especially useful for selecting modeling approaches, understanding optimization methods, interpreting results and evaluating how well a model performs.

Machine Learning and Deep Learning

ML engineers need to understand common algorithms and techniques for supervised and unsupervised learning, along with model training, validation and evaluation. Deep learning knowledge includes neural networks and architectures used for applications such as computer vision and natural language processing. 

Data Preparation and Management

Training reliable models requires high-quality data. ML engineers work with datasets to clean and transform information, select useful features and prepare data for training. SQL and data-processing tools are commonly used to retrieve, organize and work with information from different sources. 

Cloud Computing

Cloud platforms provide much of the computing infrastructure used to train and deploy ML models at scale. Familiarity with services from AWS, Microsoft Azure or Google Cloud can help engineers manage computing resources, storage, model deployment and other components of production ML applications. 

Machine Learning Operations (MLOps)

MLOps applies software development and operations practices to the ML lifecycle. Skills in areas such as model versioning, automated training and deployment pipelines, monitoring and containerization help engineers manage models consistently from development through production. 

Communication and Collaboration

ML engineers regularly work with data scientists, software engineers, product teams and other stakeholders. Effective communication is essential for explaining technical decisions, documenting work, understanding project requirements and coordinating changes across models, data and applications.

Prepare for a Career in Machine Learning Engineering 

Graduate study can offer aspiring ML engineers a structured environment for building advanced machine learning expertise while gaining experience developing AI applications. Programs that combine model development, data engineering and software engineering can help prepare students to create production-ready ML systems. 

The M.S. in Artificial Intelligence program at the Katz School of Science and Health includes coursework in machine learning, neural networks and deep learning, advanced data engineering and AI product development. The curriculum culminates in a capstone that gives students an opportunity to apply their AI knowledge through research or project-based work.

Explore the M.S. in Artificial Intelligence program to learn more about the curriculum, admissions requirements and career preparation.