How to Write a Deep Learning Resume Step-by-Step

Key takeaways

Writing a winning deep learning resume can help you stand out for a variety of jobs around AI, such as data scientist, data analyst, or machine learning engineer.

• The job outlook for professionals with deep learning skills is bright, with the deep learning market value in the United States likely to reach $63.93 billion by 2030, up from $19.42 billion in 2024 [1].

• Potential employers use your resume to assess your skills and expertise, looking for basics like experience, education, and certifications in addition to details about your proficiency with deep learning frameworks and architecture, data processing, and programming languages.

• You can highlight your machine learning skills to stand out to recruiters. 

Prepare to help fill open positions and position yourself firmly on your career path with a deep learning resume that highlights your expertise. Then, consider adding a credential to your resume by completing the Deep Learning Specialization by DeepLearning.AI. This intermediate-level five-course series is designed to help you become a machine learning expert by learning how to build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications, and more.

Deep learning job outlook

Deep learning is a part of the broader field of artificial intelligence (AI). It uses algorithms to help computers learn from data, much like the human brain learns. It is particularly helpful for tasks like image and speech recognition and completing analytical functions. Deep learning continues to grow, with a United States market that will likely reach $63.93 billion by 2030, up from $19.42 billion in 2024 [1].

With that kind of growth, we can likely anticipate consistent demand for skilled professionals in the field. The US Bureau of Labor Statistics (BLS) expects computer-related jobs to grow at a much faster rate than average across all occupations between 2024 and 2034, with 317,700 job openings per year [2]. 

What do employers look for in a deep learning resume?

Potential employers use your resume to assess your skills and expertise, looking for basics like your experience, education, and certifications in addition to details about your proficiency with deep learning frameworks and architecture, data processing, and programming languages. To stand out, it’s also essential to highlight your commitment to ongoing learning and your ability to innovate, which is vital in evolving fields like AI and deep learning. 

Although it can vary depending on the specific requirements of each job, you will likely want to demonstrate your understanding of deep learning frameworks, machine learning, programming languages, and more. Learn more about how to organize your skills in a concise and clean presentation that will get your resume noticed by hiring managers and potential employers by following these six steps to creating a compelling deep learning resume.

Step 1. Choose a deep learning resume format.

The appropriate resume format and structure can help hiring managers quickly get a sense of the details because it places focus on your strengths. In addition to clearly conveying your professional information, the resume should emphasize your areas of expertise. Three primary formats to choose from include:

Chronological: This resume format focuses first on your experience, highlighting your deep learning achievements and skills. You provide a summary of your professional background, beginning with your most recent experience and working back to older professional experience. If you have previous deep learning experience, a chronological resume is an excellent option, particularly since many employers prefer it.

Functional: A functional resume highlights your deep learning skills, making it a good option if you’re a recent graduate or new to the field and need to develop your professional deep learning experience.

Combination: The combination resume takes the best parts of a chronological and functional resume and puts them together to highlight your deep learning skills as you used them throughout your work experience.

Step 2: Craft your resume summary.

In addition to your contact information and a link to your portfolio, which can help make a positive impression on hiring managers, consider opening your resume with a well-crafted summary. Think of this section as a way of introducing yourself and your deep learning background. This brief section offers an excellent opportunity to highlight your results, accomplishments, and relevant qualifications that make you uniquely suited to the job. 

For example, you might write:

Innovative, results-focused deep learning engineer specializing in multimodal learning and generative AI. Managed an eight-person team to develop a generative adversarial network architecture to optimize model training, increasing accuracy by 30 percent. Expertise in PyTorch, natural language processing, computer vision, and CUDA programming. 

Step 3: Showcase your deep learning skills.

Hiring managers and potential employers will want to see skills specific to the field of deep learning. Highlighting your deep learning skills, throughout your experience section, and in a separate skills section can help you stand out. Consider including skills such as:

• Deep learning frameworks: Understanding of programming platforms such as PyTorch, TensorFlow, JAX, and others

• Deep learning models: Familiarity with use cases of models such as convolutional neural networks, which excel in image processing; recurrent neural networks, which support natural language processing; and deep reinforcement learning, which you can use for game-playing

• Machine learning: Applying machine learning techniques with artificial intelligence (AI) to help machines complete tasks

• Programming languages: Important programming languages to know for deep learning include Python, Java, C++, and R

• Data management: Understanding how to handle data, including cloud computing, data modeling, and big data management

In addition to a solid lineup of technical skills, you also want to highlight your workplace and transferable skills, which can demonstrate how you’ll fit into the organization. This includes skills like collaboration, creativity, adaptability, and continuous learning.

Step 4: Include your professional experience.

Outlining your prior experience in various positions offers potential employers a view into the type of tasks and responsibilities you can take on. Begin each job with the relevant details, including the name of the company, your job title, and the dates of your employment. To optimize this section, focus on your accomplishments and results as much as possible and use a varied list of action words, which could include terms like experimented, innovated, analyzed, or deployed, for example. 

This section offers an excellent chance to describe your practical experience and demonstrate the difference you made. It is also a great spot to incorporate skills and keywords, which you can find in the job listing. Keywords are essential for passing through applicant tracking systems (ATS) and moving forward in your job search. 

Step 5: Feature your certificates and certifications.

Earning additional credentials, which can include certifications and certificates, can help demonstrate your commitment to ongoing learning and upskilling, both of which are vital in evolving fields like deep learning. Adding these credentials to your resume can showcase and validate your expertise, potentially increasing your opportunities while helping you maintain a sharp, relevant skill set. 

Do some research to find the best certifications for your experience or ones that can be particularly relevant to industries or fields in deep learning. You can earn certifications from companies like Nvidia, Google, or AWS. You can also pursue non-degree educational options, such as the TensorFlow Developer Professional Certificate from DeepLearning.AI on Coursera, which can help you increase your proficiency with TensorFlow and gain practical experience using this open-source framework with CNNs and natural language processing.

Step 6: Include your education.

Although educational requirements vary among employers, providing details about your learning background can help give hiring managers a sense of your skills and foundational knowledge. 

Deep learning positions often require a college degree. According to Zippia, the most common degree for AI specialists, for example, is a bachelor’s degree, which 63 percent of workers in that position have, while 17 percent hold a master’s degree [3]. Popular areas of study include computer engineering, computer science, electrical engineering, and mechanical engineering.

Jobs that use deep learning

You can choose from different job titles and responsibilities depending on your experience in deep learning or interest in specific fields or industries. Some job titles that may require deep learning skills and their median total annual salaries include:

• Data scientist: $155,000 [4]

• Data analyst: $93,000 [5]

• Machine learning engineer: $161,000 [4]

• Natural language processing engineer: $163,000 [5]

• Artificial intelligence engineer: $141,000 [6]

All salary information represents the median total pay from Glassdoor as of April 2026. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

Summary of deep learning resume tips

• The format you choose can help highlight your expertise; choose one that fits your experience level and skill set.

• Include information relevant to potential employers, such as experience, skills, education, certifications, and other information.

• Review the job listing and incorporate skills and keywords relevant to your background into your deep learning resume.

• Limit the length of your resume to two pages while incorporating your deep learning skills, AI expertise, and notable accomplishments. 

Read more: 8 Common Deep Learning Interview Questions

Discover free resources to prepare for your next job

Subscribe to our weekly LinkedIn newsletter, Career Chat, for industry updates, tips, and trends. Then, explore more resume-building resources:

• Watch on YouTube: AI Quick Tip: Enhance Your Resume with GenAI

• Optimize your resume: Is Your Resume Passing the ATS Screening? Tips for Standing Out

• Get interview tips: How to Answer Interview Questions with the STAR Method

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