What Is a Machine Learning Engineer? What You Should Know

Artificial intelligence (AI) is actively transforming industry sectors like customer service, risk management, predictive analytics, and fraud detection—enabling organizations to reduce human error, improve quality and accuracy, and boost productivity.

Machine learning models are the reason AI technology works. These models are trained on large data sets, enabling AI systems to identify patterns, trends, and relationships. ML models can also help with tasks like anomaly detection, prediction and forecasting, natural language processing (NLP), personalization, and quality control.

Machine learning engineers are responsible for building the ML algorithms that enable AI applications to work. They also collaborate with other experts to train, debug, and deploy AI applications. With their technical expertise, machine learning engineers can help organizations make data-driven decisions, enhance customer experiences, and drive innovation.

Discover how machine learning engineers are shaping the future of AI technology, the attributes that make them successful, and how they assist businesses in reaching their potential.

Table of contents:

• What is a machine learning engineer?
• What makes a successful machine learning engineer?
• How businesses can benefit from an ML engineer
• Machine learning engineer salary and hourly rate
• How to become a machine learning engineer

What is a machine learning engineer?

A machine learning engineer specializes in designing, building, training, and deploying machine learning algorithms and models to solve real-world problems. Machine learning engineers typically work with other professionals like data scientists to collect and prepare large data sets for training. After deploying AI models, ML engineers monitor them, resolve any bugs, and make iterative improvements.

Machine learning engineers have a broad range of skills spanning from data science to software engineering. Data science skills are especially useful when collecting, cleaning, and preprocessing data. Coding expertise enables them to build machine learning models from scratch or optimize existing software using popular programming languages like Python, R, and Java.

Successful machine learning engineers also understand data structures and algorithms. They maintain a balance between theoretical knowledge and practical application in fields like deep learning and neural networks. Such knowledge comes in handy when storing, organizing, and processing data, as well as developing and optimizing machine learning models.

Role of an ML engineer

From curating data sets and tweaking algorithms to collaborating with data scientists and deploying machine learning models into real-world applications, ML engineers are deeply involved in shaping AI performance.

A machine learning engineer’s responsibilities may differ between companies and industries, but they typically revolve around the iterative nature of their work, specifically designing, testing, and refining algorithms.

Common responsibilities of a machine learning engineer include:

• Processing raw data. Machine learning engineers collaborate with other technical teams to transform raw data into actionable insights stakeholders can use for decision-making.
• Developing and training ML algorithms. Machine learning engineers use their technical skills and knowledge of programming languages like Python and Java to build ML models. They’re also responsible for training these models using large data sets to make accurate predictions and classifications.
• Running experiments and tests. Machine learning engineers experiment with AI systems and models to determine how they’ll perform in real-world situations. They then make necessary adjustments depending on the results.
• Deploying ML models. Once machine learning systems are ready, machine learning engineers work with other experts to deploy them. These professionals also monitor the performance of ML models and make optimizations.
• Conduct statistical analyses. Machine learning engineers use statistical techniques like linear regression, hypothesis testing, correlation analysis and predictive analytics to analyze trends, patterns, and relationships in data.

What makes a successful machine learning engineer?

The machine learning role is complex and requires both technical and soft skills. Skills that make ML engineers successful include:

• Deep understanding of algorithms and data structures. Algorithms and data structures serve as the foundation of machine learning systems. ML engineers must understand how these components function and fit together to create efficient ML models.
• Mastery in programming languages. Successful machine learning engineers are programmers specializing in languages like Python and Java. Python is popularly used in artificial intelligence due to its simple syntax and its wide range of libraries.
• Proficiency with ML frameworks. Creating machine learning models from scratch can be time-consuming. Machine learning frameworks add an abstract layer to the development process, allowing engineers to focus on the business logic rather than underlying principles. Successful ML engineers know how to use frameworks like PyTorch, TensorFlow, and Keras to increase their productivity.
• Analytical and problem-solving prowess. Successful ML engineers have excellent problem-solving skills. They can perform a comprehensive data analysis, identify problems, discern patterns, and offer innovative solutions.
• Growth mindset. The technology scene is always changing. Effective ML engineers maintain an open mind, constantly updating themselves with the latest AI and ML advancements.
• Adaptability. Successful machine learning engineers adapt quickly to changing conditions, allowing them to navigate the ever-evolving landscape of AI easily.
• Strong communication skills. ML engineers require excellent communication skills to work well with others, including translating complex findings to understand terms for nontechnical stakeholders.
• Collaborative nature. ML engineers work with experts like data engineers, analysts, scientists and business teams in various roles and settings. They should be able to accommodate other team players, collaborate and achieve common goals.
• Hands-on experience. Machine learning is a hands-on role requiring engineers to have practical know-how in deep learning, neural networks, and other ML techniques. This knowledge enables them to create efficient ML models capable of solving various problems.

How businesses can benefit from an ML engineer

From making informed choices to improving scalability, businesses can benefit from machine learning engineers in a variety of ways:

• Data-driven decision-making. Machine learning engineers help businesses harness large amounts of data and transform it into actionable insights. With this data-driven approach, organizations can make decisions based on evidence rather than intuition. Businesses can formulate more effective strategies that have the potential to lead to better outcomes.
• Enhanced customer experiences. Machine learning systems can analyze customer data to identify unique user behaviors, tastes, and preferences. Businesses can use this information to deliver personalized user experiences, run targeted marketing campaigns, and deliver customized recommendations. Machine learning engineers can design algorithms that adapt to user behavior in real time, leading to higher engagement and satisfaction rates.
• Streamlined operations and cost savings. Machine learning engineers help businesses minimize operating costs through the automation of repetitive and time-consuming tasks and the optimization of supply chains. They also assist in predicting equipment failures, allowing businesses to implement measures to ensure continuity. These efficiencies allow organizations to experience cost savings and employees to focus on higher-priority tasks.  
• Innovative product and service offerings. ML engineers boost innovation by enabling businesses to leverage insights from big data to create new products or improve existing ones. For example, ML engineers can assist in developing smart products or adding advanced AI features to software applications to improve their functionality.
• Improved scalability. With assistance from machine learning engineers, businesses can implement efficient data pipelines that allow them to scale operations quickly. ML engineers can help create applications capable of adapting to changing data sets and user bases with little or no human intervention.

Machine learning engineer salary and hourly rate

Machine learning engineers earn an average salary of over $151,900 per year and have an hourly rate of $25 to $50 on Upwork.

How to become a machine learning engineer

Starting with laying a strong foundation to building a robust portfolio, we explain one path toward becoming a machine learning engineer.

Machine learning career:

1. Obtain a strong educational foundation
2. Master relevant programming languages
3. Dive into machine learning fundamentals
4. Gain practical experience
5. Specialize in advanced ML techniques
6. Join ML communities and networks
7. Build a robust portfolio

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1. Obtain a strong educational foundation

Machine learning engineers analyze large data sets to discover patterns, trends, and relationships. So having a solid background in computer science, statistics, and mathematics is essential.

You can obtain a strong educational foundation by enrolling in a bachelor’s degree program for data science, computer science, statistics, or a related field. These programs can equip you with knowledge of data structures, algorithms, advanced statistical techniques, and more. You’ll also be able to develop important soft skills like communication, collaboration, and problem-solving, enabling you to thrive in a machine learning career.

Apart from attending universities and colleges, online courses, boot camps, and certifications can equip you with the necessary knowledge and skills to be a successful ML engineer. Platforms like Udemy, Udacity, and Coursera offer various machine learning and programming courses you can take.

2. Master relevant programming languages

Programming languages like Python, Java, JavaScript, and R are popular in the realm of machine learning. For example, ML engineers use Python libraries like pandas, scikit-learn, NumPy, and SciPy to analyze big data or integrate varying functionality in their applications. Java also offers different valuable dependencies to improve the development process.

As an aspiring ML engineer, you’ll want to understand how these programming languages function, including concepts like syntax, classes, variable definitions, methods, and supported libraries.

Take advantage of online boot camps and learning resources like Udemy and Coursera to acquire coding skills. You can also access hundreds of free learning materials and tutorials on W3Schools and YouTube.

As you learn software development,practice by building personal projects, participating in programming competitions, or joining boot camps. You can also collaborate with peers on various projects to enhance your programming skills.

3. Dive into machine learning fundamentals

Once you’ve mastered relevant programming languages, dive deeper into machine learning and other AI technologies to understand how they operate. Pay attention to concepts like data analytics, computer architecture, algorithms, data structures, and machine learning models since you’ll regularly interact with them in your work.

Machine learning crash courses on online platforms like EdX and freeCodeCamp can help you acquire a deeper understanding of machine learning fundamentals. Google also offers a machine learning course that takes you through real-world case studies and interactive visualizations.

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4. Gain practical experience

Building hands-on projects and real-world applications gives you the practical experience to understand what goes where—and how different components fit together.

You can gain practical experience by working on personal projects and contributing to open-source projects like on Kaggle. Internships can also allow you to work with machine learning experts to identify real-world problems and implement innovative AI solutions.

5. Specialize in advanced ML techniques

Machine learning is a broad field that encompasses advanced ML techniques like computer vision, NLP, speech recognition, predictive models, neural networks, vector machines, deep learning, and reinforced learning. It also features data collection, validation, cleaning, data modeling and transformation. And ML engineers use math methods like linear algebra, calculus, regression, and correlation to make sense of data.

Specializing in advanced ML concepts puts you in a better position to develop more robust AI apps. Consider enrolling in advanced ML courses or a master’s degree program to further your understanding of machine learning. You can also take more advanced courses on online platforms like Udacity and Udemy to gain more expertise.

As you dive deeper into machine learning, consider taking certification exams to prove your proficiency in machine learning and other AI technologies. For example, the AWS Certified Machine Learning — Specialty certification validates your expertise in developing, training, optimizing, and deploying AI models on AWS.

Machine learning certifications can add value to your portfolio, enabling you to stand out from other job applicants.

6. Join ML communities and networks

Networking helps you acquire new skills, access job opportunities, and find mentors. You also gain crucial insights and knowledge in various ML concepts by collaborating with others.

Network with other like-minded peers by joining online communities like those on Reddit, GitHub, and Stack Overflow. You can ask questions or respond to various queries on these forums, facilitating continuous learning.

Since the technological environment is ever-changing, stay updated with the latest trends and practices in the AI sector. This ensures you have the right expertise to navigate emerging situations, understand potential biases in AI, and build efficient ML applications.

Taking part in online forums and workshops and subscribing to industry newsletters can help you stay informed on the happenings in the field.‍

7. Build a robust portfolio

Once you’re confident with your skills, create a portfolio that can help showcase your skills, qualifications, and experiences to potential employers or clients.

Add some of your best projects, including personal machine learning projects, to convince businesses you have the skills they seek. You can host your projects on online repositories like GitHub and share links on your portfolio to allow clients learn more about your work

Add any certifications you’ve acquired to your portfolio to validate your expertise and enhance your professional credibility. And remember to update your portfolio regularly to show your latest skills.

Discover more tips on creating a successful portfolio to help you land a machine learning engineer role.

Take advantage of machine learning

A machine learning career can be rewarding, enabling you to work with exciting AI technologies in a competitive field.

A successful machine learning engineer should have a solid understanding of mathematics, statistics, and computer science concepts. Mastery of programming languages like Python and Java is also necessary. Soft skills like communication, teamwork, problem-solving, and analytical skills also help ML engineers thrive in their careers.

If you’re a qualified machine learning engineer looking for work, consider kick-starting your career on Upwork. You can find different machine learning jobs to help clients achieve their objectives.

And if you’re a business looking for ML experts to help integrate AI into your workflows, Upwork can connect you to machine learning professionals with varying budgets.

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Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.