The Role of a Neural Network Engineer

A neural network engineer is a machine learning or artificial intelligence engineer who designs, builds, tests, and trains neural network models. You may have many job titles as a neural network engineer, including deep learning, software, or data engineer. In this role, you’ll apply ML and AI principles to solve problems in the real world using artificial intelligence or build the infrastructure and architecture needed to support neural network systems and data. 

Learn more about what a neural network engineer does, the skills you’ll need to succeed in the field, and the average salary and job outlook for neural network professionals. 

What is a neural network engineer?

As a neural network engineer, you will be a machine learning engineer specializing in neural networks. Neural networks are AI models that allow computers to think similarly to humans: By recognizing patterns in data and learning from mistakes to determine the best action to take. As a neural network engineer, you will use machine learning concepts to design, build, train, and test algorithms for neural network models. Neural networks apply to many different industries, where you can work on projects in artificial intelligence fields, including computer vision, speech recognition, and natural language processing.

Other job titles for a neural network engineer

A neural network engineer is a type of machine learning or artificial intelligence engineer. Machine learning and AI require working with a lot of data, so ML and AI engineers are sometimes called data engineers or data scientists, even though the job titles describe different roles in the process of creating a neural network. The overlap in job roles and confusion in job titles you’ll see in job postings related to neural networks is partially because ML, AI, and neural networks are all part of a newer field still emerging. Despite how new these fields are, you can find specialized roles working directly with neural networks and creating AI-powered solutions. 

Other job titles that allow you to work directly in creating, training, testing, and designing neural network models include: 

• Test engineer

• Data engineer

• Software engineer

• Machine learning engineer

• Deep learning engineer

• Machine learning scientist

• Computational scientist

• AI research scientist

• Big data engineer

While all of these jobs refer to roles in different areas of neural networks and have different daily responsibilities, they are all examples of titles you might find doing the work of a neural network engineer: Creating and testing neural network models.

What does a neural network engineer do?

You will design, build, and test neural network models as a neural network engineer. You will work to understand the problem or application you’re creating a model for and apply AI and machine learning concepts to create and train neural networks that can provide the solution. 

As an engineer instead of a developer or researcher, you may focus on building systems of neural network models or incorporating neural network models. You may take responsibility for designing and creating the support structures that neural network models need to run properly. Depending on the size of your team, you may work to create the structure of a neural network while other AI professionals train or code the model. 

Your daily tasks as a neural network engineer may include:

• Designing neural networks using AI and ML principles to solve real-world problems

• Training, testing, and deploying AI models

• Using large data sets to select the appropriate training materials and train your model to address your prompts accurately

• Evaluating how your models behave to adjust hyperparameters or determine which model performs the best

• Collaborating with product managers, data scientists, other engineers, developers, and clients or project stakeholders. 

• Creating infrastructure for neural network production or deployment

Neural network engineer education requirements

While neural network engineer job postings might be flexible on your education requirements, you may also see a range of requested credentials. AI specialists typically hold a bachelor’s degree (63 percent), but many have a master’s degree (17 percent) [1]. Some common areas you can study include computer engineering, computer science, electrical engineering, mechanical engineering, mathematics, and statistics. Additionally, non-degree options, such as bootcamps, online courses, and certifications, can help you build your skill set.

Neural network engineer skills

If you want a career working with neural networks, you can grow your skills in areas that are common to roles in artificial intelligence. These include:

• Programming languages: As a neural network or AI engineer, you will often build your program without directly coding using resources like APIs and embedded code. You will still need a working knowledge of programming languages to work closely with neural networks. Languages like Python, C++, and HTML can help you work with neural networks. 

• Mathematics and statistics: You will need advanced math and statistics skills to work as a neural network engineer. AI models like neural networks require algebra, linear algebra, calculus, statistics, and probability, so math skills in these areas will be beneficial. 

• Deep learning frameworks: Deep learning uses neural networks to perform advanced artificial intelligence. To work with neural networks, you’ll need an understanding of machine learning and deep learning principles like supervised and unsupervised learning and types of AI models like large language models. 

• Data handling: AI models require a great deal of data for training. You’ll also need to work with data when evaluating model performance. You must understand data management, preprocessing, using big data tools, and data science concepts like exploratory data analysis. 

• Logical thinking and analytics: You will need logical thinking and analytics skills to think critically about why your model performs the way it does and what actions you can take to influence model performance. 

• Creativity: Engineering new solutions to problems using artificial intelligence sometimes takes a creative approach or thinking about problems in new ways. Developing your creativity can help you see problems from new angles. 

Neural network engineer salary and job outlook 

The average base pay for a neural network engineer in the United States is $103,382 [2]. This figure doesn’t include the additional average bonus pay for engineers, which Glassdoor lists as $24,726, bringing the total average pay for a neural network engineer to $128,108. Additional pay can include things like bonuses, profit-sharing, or commissions. 

The US Bureau of Labor Statistics (BLS) doesn’t track the field of neural network engineers specifically, but you can gain insight by looking into the growth projections for the fields of computer network architects and data scientists, two careers that intersect the work of a neural network engineer. The BLS reports that jobs for computer network architects will grow by 13 percent over the next decade, and data scientist jobs will grow by 36 percent [3, 4].

Another way to consider the growth in neural networks is to consider that the deep learning market was worth $96.8 billion and is expected to grow at a compounded annual growth rate of 31.8 percent through 2030 [5].

Learn more about neural networks on Coursera.

As a neural network engineer, you will create neural networks using AI and machine learning principles to address real-world problems. If you want to learn more about becoming a neural network engineer or a similar role, you can find Professional Certificates on Coursera to help you learn role-specific skills to prepare for an entry-level job in the field. For example, you could enroll in the IBM AI Engineering Professional Certificate or the IBM Data Engineering Professional Certificate. Or, you could focus on earning a Specialization such as the Machine Learning Specialization offered by Stanford and Deep Learning.AI.