31 Honest AI Engineer Salaries
AI isn’t just powering your favorite chatbot—it’s reshaping entire industries. Behind the scenes of this revolution are AI engineers, the specialists turning machine learning models into real-world solutions. As demand for AI skyrockets, so does the competition for top talent, and the salaries are keeping pace.
In this guide, we’ll break down what AI engineers actually do, how much they earn across the U.S., and what factors influence their pay. We’ll also cover the tools, certifications, and career moves that can help you climb the salary ladder even faster.
What is an AI Engineer?
An AI engineer is a software engineer who designs, builds, and deploys artificial intelligence models. These professionals combine data science, machine learning, and software development to create systems that can mimic human intelligence, whether they understand language, recognize images, or make predictions based on patterns in data.
Day to day, an AI engineer might clean and organize datasets, train machine learning models, test different algorithms, or integrate those models into a larger application. Their work doesn’t stop once a model is built; they’re also responsible for evaluating its performance, fine-tuning it over time, and ensuring it runs reliably at scale.
To do all this, AI engineers rely on a deep understanding of programming, statistics, and machine learning frameworks. They often use tools like TensorFlow, PyTorch, and scikit-learn, and they need to be comfortable working with cloud platforms and containerized environments.
Job titles in this space can vary, and you might see job listings with names like:
Machine learning engineer
Deep learning engineer
NLP engineer
Applied AI scientist
These roles might have slightly different focuses, but they are all rooted in the same core skill set.
31 Honest Salaries for AI Engineers
AI engineering is one of the most in-demand and well-compensated tech roles in today’s market. But how much you can actually earn depends on more than just your title. Location, experience level, and specialization all play a role in shaping your paycheck.
According to ZipRecruiter, AI engineers make around $101,000 nationwide. But the range is large, from $76,000 to $200,000+.
To give you a clear, no-fluff picture of what AI engineers really make, we compiled salary data from Glassdoor, Levels.fyi, and ZipRecruiter. We focused on base salary ranges for individual contributor roles, not inflated figures from executive positions or stock-heavy comp packages.
Below is a breakdown of low, average, and high-end salaries for AI engineers in 31 U.S. cities. This data reflects 2025 estimates and assumes full-time employment.
City / State | Low-End Salary | Average Salary | High-End Salary |
San Francisco, CA | $145,000 | $187,000 | $225,000 |
San Jose, CA | $140,000 | $180,000 | $220,000 |
Seattle, WA | $135,000 | $175,000 | $210,000 |
New York, NY | $130,000 | $170,000 | $205,000 |
Boston, MA | $125,000 | $165,000 | $200,000 |
Los Angeles, CA | $120,000 | $160,000 | $195,000 |
Washington, DC | $118,000 | $158,000 | $192,000 |
San Diego, CA | $115,000 | $155,000 | $190,000 |
Austin, TX | $110,000 | $150,000 | $185,000 |
Chicago, IL | $108,000 | $148,000 | $182,000 |
Denver, CO | $105,000 | $145,000 | $178,000 |
Atlanta, GA | $102,000 | $142,000 | $175,000 |
Portland, OR | $100,000 | $140,000 | $172,000 |
Charlotte, NC | $98,000 | $138,000 | $170,000 |
Dallas, TX | $95,000 | $135,000 | $168,000 |
Philadelphia, PA | $94,000 | $134,000 | $167,000 |
Minneapolis, MN | $93,000 | $133,000 | $165,000 |
Raleigh, NC | $92,000 | $132,000 | $163,000 |
Salt Lake City, UT | $90,000 | $130,000 | $160,000 |
Phoenix, AZ | $89,000 | $128,000 | $158,000 |
Tampa, FL | $87,000 | $126,000 | $155,000 |
Miami, FL | $86,000 | $125,000 | $153,000 |
Pittsburgh, PA | $85,000 | $124,000 | $152,000 |
Cincinnati, OH | $83,000 | $122,000 | $150,000 |
Columbus, OH | $82,000 | $121,000 | $148,000 |
Indianapolis, IN | $81,000 | $120,000 | $147,000 |
St. Louis, MO | $80,000 | $118,000 | $145,000 |
Detroit, MI | $79,000 | $117,000 | $143,000 |
Cleveland, OH | $78,000 | $116,000 | $142,000 |
Kansas City, MO | $77,000 | $115,000 | $140,000 |
Orlando, FL | $76,000 | $114,000 | $138,000 |
There’s no doubt that location has a major impact on AI engineer salaries. Cities with lots of tech companies and a high cost of living, like San Francisco, San Jose, and Seattle, top the charts. In these hubs, average salaries often exceed $170,000, with high-end roles hitting $200K+.
On the flip side, cities in the Midwest and Southeast tend to offer lower salaries, but they also have lower costs of living. For example, Cleveland and Kansas City report average salaries around $115,000 to $120,000, which can still go a long way considering the average rent for a studio apartment in those cities is $1,306 and $1,198, respectively.
A few more things stand out:
Wider salary bands in top cities suggest more variability based on specialization and seniority.
Secondary tech markets like Austin, Denver, and Charlotte continue to offer strong compensation and growing opportunities.
Remote roles aren’t listed here specifically, but many employers benchmark remote salaries against major hubs, especially if the company is headquartered there.
Ultimately, the city you live in (or work remotely for) shapes your earning potential, but it’s just one piece of the puzzle. Next, we’ll look at other factors that can impact your salary as an AI engineer.
Salary Considerations for AI Engineers
AI engineering is a specialized field, and salaries can swing wildly depending on what you bring to the table. Beyond geography, here are some of the most significant factors that impact the salary of an AI engineer.
Specialization Matters: Engineers working in deep learning, natural language processing (NLP), or computer vision often earn more than those focused on general machine learning. Niche expertise in areas like generative AI or reinforcement learning can command an even higher rate.
Academic Background Can Boost Pay: While not required, a master’s or PhD in artificial intelligence, computer science, or a related field can open doors to higher-paying roles, especially in research-heavy organizations or emerging tech startups.
Industry Plays a Huge Role: Tech companies tend to pay well, but they’re not alone. AI engineers in finance, defense, healthcare, and autonomous vehicles also earn top dollar thanks to the complexity and impact of their work.
Project Maturity Makes a Difference: If you’re helping deploy production-ready AI systems that directly affect business operations or revenue, you’re likely to earn more than someone focused on research or early-stage prototyping.
Ethical AI Is in Demand: As concerns grow around AI bias, fairness, and explainability, engineers who understand how to build responsible systems are becoming increasingly valuable—and that value often shows up in their paychecks.
How Experience Impacts Salary
Like most tech roles, AI engineering salaries grow significantly with experience. Here's how compensation typically scales over time:
Entry-Level (0–2 Years)
Entry-level AI engineers can expect to earn between $90,000 and $120,000. At this stage, most are focused on supporting model development, preparing datasets, and assisting with experimentation. They’re usually learning from more experienced colleagues and building foundational skills.
Mid-Level (3–5 Years)
With a few years under their belts, mid-level engineers earn around $120,000 to $155,000. These professionals can independently build and deploy machine learning models, mentor junior staff, and contribute to product development. They often take on more strategic or cross-functional responsibilities.
Senior-Level (6+ Years)
Senior AI engineers typically earn $155,000 to $200,000 or more. They lead complex projects, design AI architectures, and interface with leadership to align AI initiatives with business goals. At this level, soft skills like communication, leadership, and strategic thinking matter just as much as deep technical expertise.
It's also worth noting that the AI industry is in its infancy, so the salary and job responsibilities might change as the industry matures. Still, this is a solid starting point for understanding how experience can impact your salary!
Must-Know Tools for AI Engineers
AI engineers work across a broad and ever-evolving tech stack. Mastery of these tools not only boosts productivity but it can also directly influence earning potential. While the stacks might vary by industry and company, here are the main types of tools you'll want to be familiar with.
Programming and Modeling
Python is the go-to language for most AI engineers, thanks to its rich ecosystem and ease of use. R, C++, and Java also come into play, especially in roles that involve statistical modeling, performance tuning, or integration with larger systems.
Machine Learning and Deep Learning Frameworks
Popular libraries like TensorFlow, PyTorch, Keras, and scikit-learn power most machine learning workflows. These tools help engineers build, train, and evaluate models efficiently and at scale.
NLP and Computer Vision
For language tasks, libraries like Hugging Face Transformers and spaCy are essential. In computer vision, OpenCV remains a staple for image processing and analysis.
Cloud and DevOps
AI applications often need to scale, and cloud platforms make that possible. Tools like AWS SageMaker, Azure ML, and GCP AI Hub help engineers manage model training, deployment, and monitoring. On the DevOps side, Docker and Kubernetes support reproducibility and scalability.
Experiment Tracking and Versioning
Engineers rely on tools like MLflow, DVC (Data Version Control), and Weights & Biases to manage model iterations and track results over time. These platforms are especially valuable in collaborative environments and production pipelines.
Must-Have Certifications for AI Engineers
Certifications aren’t mandatory in the AI world, but they can absolutely boost your credibility, especially if you're trying to stand out in a competitive job market or pivot into AI from another tech role. The best certifications validate your skills with cloud platforms, machine learning frameworks, and real-world AI applications.
Top certifications for AI engineers include:
Google Cloud Professional Machine Learning Engineer: Focuses on designing, building, and productionizing ML models on Google Cloud. Great for engineers working in GCP environments.
AWS Certified Machine Learning – Specialty: Covers data engineering, exploratory data analysis, modeling, and deployment on AWS. Highly valued by employers for building AI solutions on Amazon’s cloud stack.
Microsoft Certified: Azure AI Engineer Associate: Designed for engineers working with Microsoft Azure tools like Azure Cognitive Services, Azure Machine Learning, and AI solutions integration.
Certified AI Practitioner (CAIP) – CertNexus: A vendor-neutral cert that covers foundational AI and ML knowledge. It’s a good option for those who want to demonstrate broad competency.
TensorFlow Developer Certificate: Focused on building and training models using TensorFlow. A solid choice for engineers working with this popular deep learning framework.
While not specific to AI, these can still strengthen your resume depending on your career path:
PMP (Project Management Professional): Valuable if you’re moving into a leadership or AI architect role.
Certified Data Scientist (DASCA): More general, but helpful if your role includes heavy analytics work.
CompTIA Data+: Good for those earlier in their data or AI careers, especially if transitioning from other IT roles.
How to Increase Your Salary as an AI Engineer
There’s no magic button to boost your income instantly, but there are clear moves that can help you climb the pay scale faster. Whether you're early in your career or looking to break into six-figure territory, these strategies can make a real difference.
Develop Deep Specialization in High-Value Areas
Expertise in trending fields like generative AI, large language models (LLMs), or time-series forecasting can set you apart. Companies are willing to pay a premium for engineers who can lead innovation in these complex, high-demand domains.
Gain MLOps and Deployment Experience
Knowing how to build models is one thing. Knowing how to deploy, monitor, and scale them in production is another. Engineers who can bridge the gap between modeling and operations (MLOps) are incredibly valuable—and often better paid.
Lead Projects With Measurable Business Impact
Want to stand out? Lead or contribute to projects that clearly move the needle—whether that’s through automation, cost savings, or new revenue streams. Being able to connect your work to bottom-line results makes you more promotable and negotiation-ready.
Build Your Reputation Through Research or Open Source
Publishing research, presenting at conferences, or contributing to open-source tools in the AI ecosystem helps establish you as a thought leader. It also builds credibility, which can lead to better offers or consulting opportunities.
Align Certifications with Your Tech Stack
Certifications from AWS, Google Cloud, or Microsoft Azure can increase your value, especially if they match the platform your employer or clients already use. Pick certs that reinforce your existing strengths and fill in any gaps.
Step Into Leadership Roles
If you’re ready, consider transitioning into an AI architect, lead engineer, or technical manager role. These positions often come with higher pay and greater influence, especially in organizations where AI is central to strategy.
Conclusion
AI engineers are among the most in-demand professionals in tech today, and the salary data proves it. Whether you're just starting out or aiming for a senior role, your earning potential depends on more than just location. Specialization, hands-on experience, and the ability to deploy production-ready systems all play a major role.
To stay competitive (and keep climbing the pay ladder), it’s essential to keep your skills sharp. That means staying current with the latest AI tools, earning relevant certifications, and continuing to learn as the field evolves.
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