10 AI Jobs That Pay Over $100,000 and the Skills You Need

Artificial intelligence is creating high-paying opportunities across software development, research, data science, cybersecurity, product management and computer hardware.

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However, the phrase “AI job” can be misleading.

Artificial intelligence is not one occupation with one salary. It is a collection of technologies used across many professions. Two people may both work with AI while performing completely different jobs.

One may train machine-learning models. Another may design the computer chips that run those models. A third may help a hospital introduce an AI system safely. A fourth may protect AI applications from cyberattacks.

Many of these careers can pay more than $100,000 annually in the United States. That does not mean every employee begins with a six-figure salary.

Compensation depends on:

Experience.
Education.
Technical ability.
Industry.
Employer.
Location.
Security clearance.
Management responsibility.
Company performance.
Bonuses and equity.
The complexity of the work.

A $100,000 salary is also not equally valuable everywhere. Taxes, housing, healthcare and transport costs can dramatically affect purchasing power.

The strongest public salary evidence often comes from broader occupations rather than fashionable titles such as “prompt engineer” or “generative AI specialist.” The US Bureau of Labor Statistics does not maintain a separate national wage category for every emerging AI role.

For that reason, this article uses established occupations that frequently include AI responsibilities.

The broader technology sector remains well paid. Computer and information technology occupations recorded a median annual wage of $105,990 in May 2024, compared with $49,500 across all occupations.

Here are 10 AI-related careers capable of crossing the $100,000 mark.

1. Machine-learning engineer

Machine-learning engineers build systems that learn from data and make predictions, classifications or automated decisions.

Their work may involve:

Preparing training data.
Selecting model architectures.
Training machine-learning models.
Evaluating accuracy.
Deploying models into applications.
Monitoring model performance.
Improving speed and reliability.
Managing cloud infrastructure.
Working with data scientists and software engineers.

Machine-learning engineering combines software development, mathematics and data engineering.

The role may be found in:

Technology companies.
Banks.
Healthcare organisations.
Manufacturers.
Retail businesses.
Logistics companies.
Media platforms.
Cybersecurity firms.
Telecommunications companies.

The Bureau of Labor Statistics does not publish a separate national median for “machine-learning engineer.” These workers are often classified within software development, data science or computer research occupations.

Software developers earned a median annual wage of $133,080 in May 2024. The highest-paid 10% earned more than $211,450.

Core skills

A machine-learning engineer commonly needs:

Python.
Data structures and algorithms.
SQL.
Statistics.
Linear algebra.
Probability.
Machine-learning frameworks.
Cloud computing.
APIs.
Software testing.
Version control.
Model deployment.
Data pipelines.

Knowledge of tools is not enough. Employers need professionals who can turn experimental models into stable products that serve real users.

Typical education

Many machine-learning engineers have a bachelor’s or master’s degree in:

Computer science.
Software engineering.
Data science.
Mathematics.
Statistics.
Electrical engineering.

A degree can help, but a strong portfolio and practical experience may also matter.

Useful projects include:

A recommendation engine.
A fraud-detection model.
A forecasting system.
An image classifier.
A document-search application.
A deployed AI service with monitoring.

The project should demonstrate not only that the model works, but that it can operate reliably after deployment.

2. AI research scientist

AI research scientists develop new methods for machine learning, robotics, language processing, computer vision and related fields.

They may investigate:

New model architectures.
Training efficiency.
Reasoning systems.
Reinforcement learning.
Robotics.
AI safety.
Interpretability.
Multimodal systems.
Human-computer interaction.
Scientific applications of AI.

This is one of the most academically demanding AI careers.

Computer and information research scientists earned a median annual wage of $140,910 in May 2024. The highest-paid 10% earned more than $232,120. BLS projects employment in the occupation to grow 20% from 2024 to 2034.

Pay can vary significantly by industry. Research scientists working for software publishers recorded a much higher median wage than those working in state colleges and universities.

Core skills

AI researchers commonly need:

Advanced mathematics.
Probability and statistics.
Optimisation.
Deep learning.
Experimental design.
Scientific writing.
Programming.
Research methodology.
Model evaluation.
Reading and reviewing academic papers.

The work involves more than using existing AI tools. Researchers are expected to create, test or improve the underlying methods.

Typical education

BLS reports that computer and information research scientists generally need at least a master’s degree in computer science or a related field. Some highly specialised research positions prefer or require a doctorate.

A research career may suit someone who enjoys:

Complex unanswered questions.
Mathematics.
Long experiments.
Reading technical papers.
Publishing results.
Working with uncertainty.

It may not suit someone who wants a quick path into technology without advanced theoretical study.

3. Data scientist

Data scientists analyse large datasets and build models that help organisations make decisions.

Their responsibilities can include:

Collecting data.
Cleaning incomplete information.
Exploring patterns.
Building predictive models.
Designing experiments.
Creating visualisations.
Presenting findings.
Measuring business outcomes.
Supporting AI applications.

Data science can be applied to:

Customer behaviour.
Healthcare outcomes.
Financial risk.
Manufacturing.
Sports performance.
Supply chains.
Marketing.
Energy.
Agriculture.
Public policy.

Data scientists earned a median annual wage of $112,590 in May 2024. The highest-paid 10% earned more than $194,410. BLS projects employment to grow 34% between 2024 and 2034, with approximately 23,400 openings per year.

Core skills

Important skills include:

Python or R.
SQL.
Statistics.
Probability.
Data visualisation.
Machine learning.
Experimental design.
Communication.
Business analysis.
Data cleaning.

The ability to explain results is essential.

A technically impressive model has limited value when executives, customers or public officials cannot understand what it means.

Typical education

BLS states that data scientists usually need at least a bachelor’s degree in mathematics, statistics, computer science or a related discipline. Some employers prefer a master’s or doctorate.

A portfolio should show complete work:

The problem.
The data source.
Data cleaning.
Analysis.
Model selection.
Evaluation.
Limitations.
Recommended action.

Copying a common online project without understanding it is unlikely to impress a serious employer.

4. AI software developer

AI software developers build the applications through which users interact with artificial intelligence.

Examples include:

AI assistants.
Search tools.
Recommendation systems.
Translation applications.
Automated customer support.
Document-analysis platforms.
Medical software.
Fraud-detection systems.
Creative tools.
Robotics software.

They may not create foundation models from the beginning. Instead, they connect models with databases, user interfaces, business rules and security systems.

Software developers earned a median annual wage of $133,080 in May 2024. BLS projects strong demand partly because organisations need developers to create AI-based business solutions and maintain increasingly complex systems.

Core skills

AI software developers may need:

Python, JavaScript, Java, C++ or another language.
APIs.
Databases.
Cloud platforms.
Software architecture.
Testing.
Cybersecurity.
User authentication.
AI model integration.
Monitoring.
Version control.
Cost optimisation.

AI applications can create unusual software problems.

Model outputs may be inconsistent. Prompts can be manipulated. Usage costs can rise unexpectedly. Sensitive data can be exposed when controls are weak.

A capable developer must therefore think beyond a successful demonstration.

Education and entry

A bachelor’s degree in computer science or a related field is common, although employers may also consider strong practical experience.

A useful portfolio could include an AI application that:

Solves a clear problem.
Uses secure authentication.
Stores data responsibly.
Measures output quality.
Handles errors.
Includes automated tests.
Controls model costs.
Has clear documentation.

5. Computer vision engineer

Computer vision engineers create systems that interpret images and video.

Applications include:

Medical imaging.
Autonomous vehicles.
Manufacturing inspection.
Facial recognition.
Agriculture.
Satellite analysis.
Sports.
Security.
Robotics.
Retail inventory systems.

The job may combine machine learning with software engineering, optics, image processing and hardware knowledge.

Computer vision engineers may fall within several established occupations, including software developers, data scientists and computer research scientists. Those categories reported median annual wages of $133,080, $112,590 and $140,910 respectively in May 2024.

Core skills

Employers may seek:

Python.
C++.
Linear algebra.
Image processing.
Deep learning.
Object detection.
Image segmentation.
Camera systems.
Model optimisation.
Edge computing.
Dataset labelling.
Model evaluation.

Computer vision requires careful attention to data quality.

A system trained on narrow or unrepresentative images may perform poorly in different lighting, weather, locations or populations.

Education

Common backgrounds include:

Computer science.
Electrical engineering.
Robotics.
Mathematics.
Physics.
Data science.

A graduate degree may be helpful for research-heavy positions, while applied roles may place greater weight on deployment experience.

6. Natural-language processing engineer

Natural-language processing engineers develop systems that analyse or generate human language.

Their work may support:

Chatbots.
Translation.
Speech recognition.
Search.
Summarisation.
Sentiment analysis.
Document classification.
Question-answering systems.
Voice assistants.
Legal and medical text analysis.

Generative AI has increased demand for professionals who can connect language models to reliable business systems.

As with machine-learning engineering, there is no separate BLS wage category for natural-language processing engineers. These workers are often classified as software developers, data scientists or computer research scientists—occupations with median wages above $100,000.

Core skills

The role may require:

Python.
Linguistic concepts.
Machine learning.
Transformer models.
Information retrieval.
Vector search.
Prompt and context design.
Model evaluation.
Data preparation.
API development.
Software security.

A serious language-AI system must do more than produce impressive sentences.

It may also need to:

Cite evidence.
Protect confidential data.
Refuse unsafe requests.
Detect unsupported answers.
Work across languages.
Follow business rules.
Operate within cost limits.

Education

Common backgrounds include:

Computer science.
Computational linguistics.
Data science.
Mathematics.
Language technology.

A doctorate is not always necessary. However, research positions developing new language models may require advanced study.

7. AI cybersecurity specialist

Artificial intelligence creates both security opportunities and security threats.

Cybersecurity professionals may use AI to:

Detect unusual activity.
Analyse malware.
Prioritise alerts.
Identify fraud.
Monitor networks.
Automate threat investigation.

They must also protect AI systems against:

Data poisoning.
Prompt injection.
Model theft.
Sensitive-data exposure.
Manipulated training data.
Malicious automated activity.
Insecure third-party tools.

Information security analysts earned a median annual wage of $124,910 in May 2024. The highest-paid 10% earned more than $186,420.

Core skills

An AI cybersecurity specialist may need:

Network security.
Cloud security.
Secure software development.
Identity and access management.
Incident response.
Python.
Threat modelling.
Machine learning.
Data privacy.
Security testing.
Risk assessment.

This career requires constant learning because attack methods and AI systems change quickly.

Education and certifications

Common educational backgrounds include:

Cybersecurity.
Computer science.
Information technology.
Computer engineering.

Professional certifications can help, but they do not replace practical ability.

Employers may value experience with:

Security operations.
Penetration testing.
Cloud environments.
Incident handling.
Governance and compliance.

8. AI product manager

AI product managers decide which artificial-intelligence products should be built and how they should create value for users.

They connect:

Customers.
Engineers.
Data scientists.
Designers.
Legal teams.
Security teams.
Executives.
Sales and marketing.

Their responsibilities may include:

Defining the problem.
Prioritising features.
Evaluating business value.
Measuring product performance.
Managing risks.
Coordinating product launches.
Monitoring customer feedback.
Setting long-term strategy.

There is no specific national BLS category for AI product managers.

Depending on responsibility, they may be classified within computer and information systems management or related management occupations.

Computer and information systems managers earned a median annual wage of $171,200 in May 2024.

This does not mean a first-time product manager should expect that amount. Management positions usually require years of relevant experience.

Core skills

AI product managers need:

Product strategy.
Customer research.
Data analysis.
Technical understanding.
Communication.
Project prioritisation.
Risk management.
Model evaluation.
Business finance.
Responsible-AI awareness.

They do not always need to train models personally, but they must understand enough to challenge unrealistic claims.

An AI product manager should be able to ask:

What problem does this system solve?
How will success be measured?
What data does it use?
What happens when it is wrong?
What does it cost to operate?
Which users could be harmed?
Is AI genuinely necessary?

9. AI systems architect

AI systems architects design the technical structure that connects models, data, software, cloud infrastructure and security.

They may determine:

Where data is stored.
Which models are used.
How applications communicate.
How systems scale.
How sensitive information is protected.
How performance is monitored.
How failures are handled.
How costs are controlled.

This role is particularly important when a company moves from a small AI experiment to a system serving thousands or millions of users.

Systems architects may be classified within software development, computer systems analysis, database architecture or information systems management.

Computer systems analysts earned a median annual wage of $103,790 in May 2024.

Experienced architects and technology managers can earn considerably more, but reaching the role normally requires a strong history in software, cloud systems or enterprise technology.

Core skills

Important skills include:

Software architecture.
Cloud computing.
Databases.
Distributed systems.
APIs.
Data engineering.
Cybersecurity.
Machine-learning operations.
Reliability.
Cost management.
Technical documentation.

Career route

A person does not usually enter technology as a senior AI architect.

A typical path may involve:

Software development or infrastructure work.
Cloud and database experience.
Designing larger systems.
Leading technical projects.
Learning AI deployment and governance.
Moving into architecture responsibility.

10. AI hardware engineer

AI requires physical computing infrastructure.

Hardware engineers design and improve:

Processors.
Accelerators.
Servers.
Memory systems.
Networking equipment.
Robotics components.
Edge-computing devices.
Data-centre hardware.

AI models demand significant computing power, making hardware efficiency strategically important.

Computer hardware engineers earned a median annual wage of $155,020 in May 2024. The highest-paid 10% earned more than $223,820.

Core skills

AI hardware work may require:

Electrical engineering.
Computer architecture.
Digital logic.
Semiconductor design.
Embedded systems.
C or C++.
Hardware-description languages.
Performance optimisation.
Parallel computing.
Thermal and power management.

Education

A bachelor’s degree in computer engineering, electrical engineering or a related field is a common entry requirement.

Advanced design and research roles may require graduate study.

This career is less accessible through a short online course than some software roles because it depends heavily on engineering fundamentals and specialised tools.

Other AI careers that can cross $100,000

The AI economy also supports high-paying work beyond the 10 roles above.

Examples include:

Statistician.
Applied mathematician.
Database architect.
Robotics engineer.
Cloud engineer.
AI governance specialist.
Technology consultant.
Technical sales engineer.
AI programme manager.
Quantitative analyst.
AI solutions consultant.

Mathematicians earned a median annual wage of $121,680 in May 2024, while statisticians earned $103,300.

Management analysts, a category that can include experienced technology and AI consultants, earned a median annual wage of $101,190.

Not every employee in these categories works with AI, and not every worker earns more than $100,000.

The figures show that the broader professional foundations supporting AI can produce six-figure careers.

What “pays over $100,000” really means

Salary articles can create unrealistic expectations when they omit important details.

Median pay is not starting pay

The median is the point at which half of workers earn more and half earn less.

It includes experienced professionals.

A graduate entering the field may earn considerably below the median.

Total compensation is not the same as salary

Some technology companies advertise compensation that combines:

Base salary.
Annual bonus.
Company shares.
Signing bonus.
Commission.
Benefits.

A package valued at $160,000 may include a $110,000 base salary and stock that changes in value.

Location matters

Salaries may be higher in expensive technology centres, but housing and taxes may also be much higher.

Remote employees can also receive pay based on their location.

Industry matters

AI professionals in software, finance and specialised research may earn more than workers performing similar tasks in education, government or smaller nonprofits.

Experience matters

Senior professionals who lead systems or teams can earn much more than junior employees.

A six-figure career should therefore be viewed as a realistic long-term possibility—not an automatic first salary.

Do you need a degree?

The answer depends on the role.

A degree is especially important for:

AI research.
Computer hardware engineering.
Advanced statistics.
Robotics research.
Scientific machine learning.

Software and applied AI roles may offer more flexible entry routes.

Some employers consider candidates with:

Strong portfolios.
Professional experience.
Open-source contributions.
Industry certifications.
Technical assessments.
Demonstrated business results.

However, “no degree required” does not mean “no education required.”

A self-taught candidate must still learn:

Programming.
Mathematics.
Databases.
Software engineering.
Data analysis.
Technical communication.

Without a degree, evidence of ability becomes even more important.

Skills shared across high-paying AI jobs

Programming

Python is widely used in AI and data science.

Other important languages may include:

SQL.
JavaScript.
Java.
C++.
R.
Go.
Rust.

The right language depends on the role.

Mathematics

Core areas include:

Algebra.
Probability.
Statistics.
Calculus.
Linear algebra.
Optimisation.

Not every position requires advanced theory, but mathematical understanding helps professionals evaluate models rather than merely run tools.

Data

AI systems depend on data.

Professionals need to understand:

Collection.
Cleaning.
Storage.
Labelling.
Privacy.
Quality.
Bias.
Governance.

Software engineering

Employers need reliable systems, not only notebooks and demonstrations.

Important practices include:

Testing.
Version control.
Documentation.
Security.
Monitoring.
Error handling.
Code review.

Communication

High-paying professionals explain technical decisions to people who may not be technical.

They must communicate:

Costs.
Risks.
Limitations.
Expected benefits.
Uncertainty.
Project progress.

A practical roadmap into a six-figure AI career

Step 1: Choose one role

Do not attempt to learn every AI specialisation simultaneously.

Choose a direction such as:

Machine-learning engineering.
Data science.
AI software development.
Cybersecurity.
Research.
Product management.
Hardware engineering.

Step 2: Study the foundations

Learn the underlying skills before chasing advanced tools.

For technical roles, this usually includes:

Programming.
Data structures.
SQL.
Statistics.
Mathematics.
Software development.

Step 3: Build complete projects

A useful project should solve a problem from beginning to end.

Do not show only a model.

Show:

Data preparation.
Architecture.
Evaluation.
User interface.
Deployment.
Testing.
Documentation.
Limitations.

Step 4: Gain real experience

Experience may come from:

Internships.
Entry-level roles.
Freelancing.
Open-source work.
University research.
Volunteer projects.
Building tools for a small business.
Internal projects at an existing employer.

Step 5: Learn the industry

An AI professional with banking knowledge can be more valuable to a financial institution than a technically stronger candidate who does not understand financial risk.

AI careers exist in almost every industry.

Domain knowledge matters in:

Healthcare.
Finance.
Law.
Manufacturing.
Agriculture.
Logistics.
Media.
Energy.

Step 6: Document measurable results

Instead of writing “worked on an AI model,” explain the outcome.

Examples include:

Reduced processing time.
Improved forecast accuracy.
Lowered cloud cost.
Detected more fraudulent transactions.
Increased customer completion rates.
Automated a manual workflow.

Do not invent figures.

Use only results that can be explained and defended.

Step 7: Practise interviews

Technical interviews may involve:

Programming.
Algorithms.
Statistics.
Machine-learning concepts.
System design.
Product cases.
Previous projects.

Senior roles may focus more heavily on architecture, leadership and trade-offs.

Can a short AI course lead directly to $100,000?

Usually not by itself.

A short course can:

Introduce concepts.
Provide structured practice.
Help create an initial project.
Demonstrate motivation.

It does not replace years of knowledge required for advanced engineering, research or leadership roles.

Be cautious of training providers claiming that anyone can complete a few weeks of study and immediately earn a six-figure salary.

The salary is paid for the ability to solve expensive problems—not for possessing a certificate with “AI” in its title.

Is prompt engineering a stable six-figure career?

Prompt design is a useful skill, but “prompt engineer” should not be treated as a guaranteed long-term career category.

Writing effective instructions for AI can be part of:

Software development.
Product management.
Marketing.
Research.
Customer support.
Data analysis.
Automation.

The stronger career strategy is to combine prompt skills with a durable professional foundation.

For example:

A software developer who can build AI applications.
A lawyer who can evaluate legal AI tools.
A marketer who can automate content analysis.
A data scientist who can evaluate model outputs.
A cybersecurity professional who can test AI systems.

Tools and prompting methods will change. Deeper expertise remains valuable.

AI jobs without heavy coding

Not every well-paid AI career requires programming throughout the day.

Roles with less coding may include:

AI product manager.
AI programme manager.
Technology consultant.
AI governance specialist.
Technical sales professional.
Risk and compliance specialist.
AI policy analyst.

However, these roles usually require another form of expertise.

An AI product manager needs product experience. A consultant needs business judgment. A governance specialist needs knowledge of law, risk, privacy or compliance.

Avoiding coding does not mean avoiding difficult skills.

Mistakes that prevent people from entering AI careers

Learning tools without foundations

A framework may become outdated. Programming, statistics and problem-solving remain useful.

Collecting certificates without building projects

Employers want evidence that knowledge can be applied.

Copying tutorial projects

A project has little value when the candidate cannot explain each decision.

Ignoring communication

Technical ability alone may not lead to senior responsibility.

Applying only to companies with “AI” in their names

Banks, hospitals, manufacturers and governments also hire AI professionals.

Expecting a six-figure starting salary

Career progression usually requires time and proven results.

Believing every online salary claim

Some figures combine salary, shares and bonuses or refer to unusually expensive locations.

Future outlook

AI is likely to change the composition of technology work rather than create only one new profession.

BLS expects demand for software developers to remain strong as organisations build AI-based solutions and maintain increasingly complex data systems.

Data science is projected to grow 34% between 2024 and 2034, while computer and information research science is projected to grow 20%.

The wider STEM workforce is projected to grow faster than non-STEM employment. BLS reported a 2024 median wage of $103,580 for STEM occupations, more than twice the $48,000 median for non-STEM work.

This does not mean AI will protect every technology job.

Automation may reduce demand for some repetitive tasks. BLS projects employment for computer programmers to decline 6% from 2024 to 2034 even as demand remains stronger for software developers who design broader applications and systems.

Professionals should therefore aim to provide value beyond writing routine code.

Future advantages may come from:

System design.
Domain expertise.
Cybersecurity.
Responsible AI.
Research.
Product judgment.
Communication.
Leadership.
Understanding customers.

Conclusion

AI careers can pay more than $100,000, but the salary is not produced by learning one fashionable tool.

The strongest six-figure opportunities include:

Machine-learning engineer.
AI research scientist.
Data scientist.
AI software developer.
Computer vision engineer.
Natural-language processing engineer.
AI cybersecurity specialist.
AI product manager.
AI systems architect.
AI hardware engineer.

These careers demand different combinations of programming, mathematics, business knowledge, research and communication.

The clearest path is to choose one role, master its foundations, build complete projects and gain experience solving real problems.

Do not build an entire career around a temporary tool or job title.

Build expertise that remains useful as technology changes.

Disclaimer: Salary information in this article is based primarily on US occupational data and does not guarantee individual compensation. Actual pay varies by location, experience, education, employer, industry and compensation structure. Career and educational decisions should be based on personal circumstances and current local labour-market information.

Sources consulted

U.S. Bureau of Labor Statistics — Data Scientists.
U.S. Bureau of Labor Statistics — Computer and Information Research Scientists.
U.S. Bureau of Labor Statistics — Software Developers, Quality Assurance Analysts and Testers.
U.S. Bureau of Labor Statistics — Information Security Analysts.
U.S. Bureau of Labor Statistics — Computer and Information Technology Occupations.
U.S. Bureau of Labor Statistics — Computer and Information Systems Managers.
U.S. Bureau of Labor Statistics — Computer Systems Analysts.
U.S. Bureau of Labor Statistics — Mathematicians and Statisticians.
U.S. Bureau of Labor Statistics — Computer Hardware Engineers.
U.S. Bureau of Labor Statistics — AI Impacts in Employment Projections.
U.S. Bureau of Labor Statistics — STEM Employment Projections.
U.S. Bureau of Labor Statistics — Computer Programmers.