What Is an AI Research Scientist? 2025 Career Guide

Artificial intelligence (AI) is impacting how we live and work. AI scientists have made huge advancements in machine learning, deep learning, and generative AI, reducing the need for human input in everyday tasks and beyond. In 2024, the AI market was worth $184 billion, with predictions for it to grow to $826 billion by 2030 [1].

Learn about the role of an AI research scientist in this growing market and how these professionals play an integral part in shaping this new technology. Discover the steps to start a career in this field.

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What does an AI research scientist do?

As an AI research scientist, you'll shape AI advancements by researching and analyzing information that can help progress AI developments. Work involves creating methodologies and experimental designs to solve complex problems and create or improve algorithms. It should also support machines while they actively learn, react, and develop.

AI research scientist tasks and responsibilities 

As an AI research scientist, you’ll perform a range of tasks that vary according to where you work and your industry. Most duties and responsibilities fall into four categories: research, algorithm development, publication, and collaboration.

1. Research

You’ll research methodologies and algorithms to improve the performance of AI systems and use findings to aid in product development by designing and developing prototypes to evolve. Research scientists use experiments to study and analyze datasets while keeping up with and understanding industry trends and developments.

2. Algorithm development

You’ll develop, test, and improve machine learning algorithms, write code, train computer systems, and create models of human behavior to enhance AI capabilities. An example of this might be designing an algorithm to analyze medical images effectively to aid in the early detection of illnesses.

3. Collaboration

You’ll stay updated with trends and developments by collaborating with the wider AI community, working as part of a multidisciplinary team of engineers, data scientists, and machine learning experts.

4. Publication

As a scientist, your work is both practical and academic. Part of your role could include publishing research papers and theoretical advances in academic journals. 

Types of AI research scientist

As an AI researcher, you have a range of options regarding the industry you focus on; AI is advancing in every field. For example, in retail, you’ll find self-service checkouts. In health care, you’ll see advances like CT image reconstruction. Manufacturing utilizes autonomous mobile robots. Industries you might consider focusing on include:

• Finance

• Health care

• Entertainment

• Technology

• Manufacturing

• Education

• Energy

• Defense 

AI research scientist skills

AI research scientists have a multitude of technical skills necessary for conducting research and building AI algorithms and models. Workplace skills are also important as your work includes collaborating with others and reporting on your findings. Essential skills include the following

Technical skills:

• Statistical analysis

• Mathematical modeling

• Algorithm development 

• Programming

• Machine learning 

• Research ethics

• Natural language processing (NLP)

• Deep learning frameworks

• Big data technologies

Workplace skills:

• Research

• Analytical skills

• Problem-solving

• Critical thinking

• Communication

• Collaboration

The importance of AI research ethics

As AI technology continues to grow and develop, it’s integral that as an AI research scientist, you have ethical judgment and a sense of social responsibility that you apply to your work. It’s essential that with every new development you make, you consider the wider impact on humanity, ensuring that your work benefits society and that professionals in the field are accountable. 

AI research scientist salary and job outlook

AI is an exciting field, with an excellent job outlook and high salaries. AI professionals, including AI research scientists, are in high demand. According to the US Bureau of Labor Statistics (BLS), computer and information research scientists are in demand, with a job outlook of 26 percent between 2023 and 2033, which is much higher than average [2]. The demand for AI professionals in all fields continues to grow as the technology develops rapidly.

According to the BLS, the median annual salary for a research scientist in the computer and information technology industry is $145,080 [2]. Glassdoor reports the average annual base salary for an AI research scientist is $118,090, which rises to $174,525 with bonuses and additional payments [3].

How to become an AI research scientist

AI research scientists are in high demand, and employers are searching for candidates with the required technical ability. For this role, you need a combination of relevant skills, including some experience in research, AI expertise, and a high standard of education. 

1. Education and training

To work as an AI research scientist, a bachelor’s degree is the first step. Choose a degree that incorporates the technical and mathematical elements you’ll need for this role. Examples include computer science, mathematics, physics, and engineering. 

Following a bachelor’s degree, some choose to work towards advanced degrees in a more specialized field, such as a master’s degree in AI and machine learning or even a PhD. 

Whatever degree you choose, your courses must cover topics lik:

• Algorithms

• Statistics

• Data structures

• Programming

• Algebra

• Calculus

If you don’t focus on these as part of your degree, work on them independently by taking an online course, boot camp, or certification. 

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2. AI certifications

Certifications are an excellent way of demonstrating your knowledge to an employer. In an industry like AI, where advancements happen quickly, it’s even more important to keep up with trends and developments. 

Certifications in AI will allow you to move beyond your academic studies, helping you develop and showcase specialist knowledge in areas such as robotics, machine learning, big data, engineering, and algorithms. Examples of AI certifications to consider include:

• Amazon Certified Machine Learning Certificate

• Certified Artificial Intelligence Specialist (CAIS)

• Certified Artificial Intelligence Engineer (CAIE)

3. Experience 

Experience and skill development are important preparation for a job as an AI research scientist. If you can take an internship, take it to build your industry experience and AI practical skills. You might also engage in collaborative projects and join AI clubs to build your skills.

4. Career advancement

Once you have honed your AI skills and experience, you’ll find a range of career options available. You might move into other AI roles such as AI developer, machine learning engineer, deep learning engineer, big data engineer, or you can use your research skills to transition into jobs like data scientist or computer and information systems manager.

Build your AI research skills on Coursera

The field of AI is continually growing and may have many opportunities if you have the right education, experience, skills, and knowledge. If you’d like to start your career in AI, consider upgrading your skills with an online course or Professional Certificate. You could check out the IBM AI Foundations for Everyone Specialization on Coursera. Upon completion gain a certificate to include in your resume, CV, or LinkedIn profile.

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Google IT Support

Get on the fast track to a career in IT. In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.

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(182,899 ratings)

1,746,895 already enrolled

Beginner level

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Average time: 6 month(s)

Learn at your own pace

Skills you'll build:

Cybersecurity, Wireless Security, Cryptography, Network Security, Directory Service, Lightweight Directory Access Protocol (LDAP), Backup, Domain Name System (DNS), Ipv4, Network Model, Troubleshooting, Binary Code, Customer Support, Linux, Powershell, Linux File Systems, Command-Line Interface