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32 Honest Machine Learning Engineer Salaries

machine learning engineer Salary-Blog
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Published on June 16, 2025

Machine learning engineers are shaping the future—whether they’re powering recommendation systems, optimizing logistics, or helping generative AI get smarter. As companies race to add artificial intelligence to their stacks, the demand for skilled ML engineers has exploded.

But despite the hype, salary expectations in this field aren’t always easy to figure out. Titles vary, job descriptions blur the line between data science and engineering, and compensation can swing dramatically based on your tech stack, industry, and experience level.

In this guide, we’ll break down what machine learning engineers actually do, how much they earn in different U.S. cities, and what factors (like tools, cloud experience, and production deployment) can boost your salary.

What is a Machine Learning Engineer?

Machine learning engineers build and deploy the models that help computers learn from data—sometimes massive amounts of data. They’re part software developer, part data scientist, and are responsible for taking machine learning algorithms out of notebooks and turning them into real, production-ready systems. 

While data scientists often focus on research and exploration, machine learning engineers make that work usable in the real world. Depending on the company, the role may be called: 

  • Machine learning engineer

  • Applied machine learning engineer

  • AI/ML engineer

  • MLOps engineer

  • Data scientist – ML focus

  • AI engineer

Want to learn more? Check out our updated Introduction to Machine Learning Online Training. 

Core Responsibilities

Machine learning engineers typically handle the full model development lifecycle—from selecting the right algorithms to training and testing models to deploying them into production environments. 

Along the way, they work closely with data scientists, data engineers, DevOps teams, and product stakeholders to align ML models with business goals.

Their responsibilities often include:

  • Preprocessing large datasets

  • Training and tuning models

  • Building and maintaining scalable ML pipelines

  • Integrating models into apps or APIs

  • Monitoring model performance and retraining as needed

Key Skills and Tools

Most ML engineers code in Python, often using libraries like scikit-learn, TensorFlow, and PyTorch. They also work with tools for deployment (Docker, FastAPI, AWS Lambda), data processing (SQL, Pandas, Spark), and pipeline orchestration (Airflow, MLflow, Kubernetes). We'll discuss the rest of the tools machine learning engineers should know in a later section.  

32 Honest Salaries for Machine Learning Engineers

Machine learning engineers are some of the most in-demand tech professionals today, but figuring out exactly what they earn can be surprisingly difficult. Nationally, machine learning experts make an average of $128,000 per year

But job titles vary from company to company, and salary ranges often depend on whether you're focused on research, production deployment, or model infrastructure. Location, skills, and even industry can all add to the confusion. 

To give you a clearer picture, we pulled base salary estimates from ZipRecruiter and benchmarked them against real-world job postings on Indeed, Glassdoor, and Salary.com. We reviewed listings for job titles like machine learning engineer, AI engineer, and applied ML engineer in 32 U.S. cities. Then we averaged the results into low, mid, and high-end salary bands. Here are the results: 

City / State

Low-End Salary

Average Salary

High-End Salary

San Jose, CA

$115,000

$150,000

$190,000

Seattle, WA

$110,000

$145,000

$185,000

New York, NY

$108,000

$142,000

$180,000

Boston, MA

$105,000

$140,000

$175,000

Los Angeles, CA

$102,000

$136,000

$170,000

Austin, TX

$98,000

$130,000

$165,000

Chicago, IL

$95,000

$128,000

$160,000

Denver, CO

$94,000

$125,000

$158,000

Atlanta, GA

$92,000

$122,000

$155,000

Dallas, TX

$94,000

$125,000

$158,000

Philadelphia, PA

$90,000

$120,000

$152,000

Raleigh, NC

$88,000

$118,000

$150,000

Phoenix, AZ

$86,000

$115,000

$145,000

Salt Lake City, UT

$85,000

$114,000

$143,000

Minneapolis, MN

$88,000

$118,000

$148,000

Portland, OR

$90,000

$120,000

$150,000

Houston, TX

$92,000

$122,000

$155,000

Charlotte, NC

$86,000

$115,000

$145,000

Orlando, FL

$84,000

$112,000

$140,000

Indianapolis, IN

$82,000

$110,000

$138,000

Columbus, OH

$83,000

$111,000

$139,000

Baltimore, MD

$85,000

$114,000

$144,000

San Antonio, TX

$82,000

$109,000

$135,000

Tampa, FL

$83,000

$110,000

$137,000

Des Moines, IA

$78,000

$104,000

$130,000

Nashville, TN

$80,000

$108,000

$135,000

Boise, ID

$77,000

$102,000

$128,000

Kansas City, MO

$79,000

$105,000

$132,000

Macon, GA

$75,000

$98,000

$122,000

Tallahassee, FL

$74,000

$96,000

$120,000

Omaha, NE

$76,000

$100,000

$125,000

Albuquerque, NM

$75,000

$98,000

$123,000

What does all this data tell us? Here are a few trends that stand out. 

  • Tech Hubs Still Dominate: Cities like San Jose, Seattle, and New York consistently offer the highest salaries, with top-end pay exceeding $180K. These regions are home to many AI-first companies and major tech employers willing to pay a premium for top talent.

  • Smaller Cities Still Offer Solid Pay: Although Des Moines, Boise, and Tallahassee are lower on the chart, they still offer six-figure potential. This is especially attractive when paired with a lower cost of living in those areas. 

  • Wide Salary Ranges Reflect Experience and Specialization: Most cities show a $40K–$60K spread between entry-level and senior-level roles. That gap reflects differences in production experience, cloud fluency, and the ability to lead or scale ML initiatives. Good news: these are things you have control over. 

  • Cloud and MLOps (Machine Learning Operations) Experience Boosts Pay: Job listings that mention deployment, monitoring, or cloud integration often sit at the upper end of the pay scale. Employers are actively looking for engineers who can do more than just build models. 

  • R&D and High-Stakes Industries Pay More: Due to their complexity and impact, ML roles in fintech, healthcare AI, autonomous systems, and defense frequently post salaries above the national average. 

Salary Considerations for Machine Learning Engineers

Obviously, location can influence compensation for machine learning engineers, but there are a number of other factors that affect your take-home pay. The key factors that tend to push ML engineer salaries up (or down) include: 

Technical Stack

If you're skilled in popular ML libraries like TensorFlow, PyTorch, or scikit-learn, you’re already ahead—but engineers with production-level experience in MLOps tooling (like MLflow, Kubeflow, or SageMaker) tend to command even higher salaries.  

Industry

Your paycheck can look very different depending on the type of company you work for. AI-first startups, autonomous systems, fintech, healthcare AI, and defense contractors often offer higher compensation than academic research labs, nonprofits, or edtech.

Academic Background

While it’s possible to break into ML engineering with a strong portfolio and a bachelor’s degree, employers in research-heavy roles often prefer (and pay more for) candidates with a Master’s or PhD in computer science, data science, statistics, or a related field.

Cloud Fluency

Knowing your way around AWS, Google Cloud (GCP), or Microsoft Azure—especially their ML services—can boost your market value significantly. Many companies now expect ML engineers to build and deploy models directly into the cloud.

Production Experience

Building a model is one thing; getting it into production is another. Engineers who have deployed ML models in real applications, set up monitoring, and handled retraining workflows tend to be more sought after and better paid.  

How Experience Impacts Salary

As with most tech careers, experience is also a major factor in determining salary for machine learning engineers. The more hands-on projects you’ve tackled—and the more stages of the machine learning lifecycle you’ve owned—the more valuable you become. Here’s how compensation typically scales with experience:

Entry-Level (0–2 Years)

Typical Salary Range: $85,000–$110,000

Entry-level ML engineers usually work under the guidance of senior engineers or data scientists. Their tasks may include data cleaning, feature engineering, model tuning, and helping to optimize existing pipelines. They might also assist in research, documentation, or implementation of models designed by others.

Mid-Level (3–5 Years)

Typical Salary Range: $110,000–$140,000

With a few years of experience, ML engineers often take on end-to-end responsibilities—training models, deploying them to production, collaborating cross-functionally with data engineers and product managers, and integrating ML into business processes.

Senior-Level (6+ Years)

Typical Salary Range: $140,000–$180,000+

Senior engineers lead entire machine learning initiatives. They may architect model pipelines, develop custom algorithms, manage teams, and interface with stakeholders to align ML strategy with business goals. Many contribute to long-term technical vision or innovation roadmaps.

Must-Know Tools for Machine Learning Engineers

While your specific stack may depend on the company or industry, most ML roles expect fluency in key programming languages, libraries, and deployment platforms. Below are the core categories of tools you should know about.

Languages & Libraries

At the heart of machine learning is code, and Python is the clear favorite. Most engineers also use:

  • R: Still valuable in statistical modeling and research environments.

  • TensorFlow and PyTorch: Two of the most popular deep learning frameworks.

  • scikit-learn: A go-to for traditional machine learning models.

Modeling Platforms

These tools help engineers develop, version, and manage machine learning experiments:

  • Jupyter Notebooks: A staple for interactive development and prototyping.

  • MLflow: Popular for experiment tracking, model packaging, and deployment.

  • DVC (Data Version Control): A Git-like tool for managing datasets and ML pipelines.

Deployment Tools

Once models are trained, they need to be deployed into real-world systems. That’s where these come in:

  • Docker and Kubernetes: Enable scalable, containerized deployment of models.

  • AWS Lambda and FastAPI: Useful for lightweight, serverless deployment of inference endpoints.


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Data Tools

Good models require great data. These tools help with data processing and orchestration:

  • SQL and Pandas: Essential for data wrangling and exploratory data analysis.

  • Apache Spark: For distributed data processing at scale.

  • Airflow: Manages data pipelines and scheduling across systems.

Monitoring & MLOps Platforms

Maintaining models in production is just as important as building them. These tools support performance tracking and automation:

  • Prometheus and Grafana: Used for system monitoring and alerting.

  • Amazon SageMaker and Azure ML Pipelines: Provide managed services for building, training, and monitoring models at scale.

Must-Have Certifications for Machine Learning Engineers

While a strong portfolio and traditional degrees often speak louder than a certificate, earning an industry-recognized cert can give you an edge, especially when applying to larger companies or transitioning into ML from another role. Here are some of the most respected certifications in the field:

TensorFlow Developer Certificate (Google)

This certification validates your ability to use TensorFlow to build, train, and deploy ML models. It's ideal for those looking to demonstrate practical skills in one of the most popular deep learning frameworks.

Best for: Entry- to mid-level engineers or developers pivoting into machine learning.

AWS Certified Machine Learning – Specialty

Offered by Amazon Web Services, this certification proves you can design, implement, and maintain ML solutions in the AWS Cloud. It covers everything from data engineering to deploying scalable models.

Best for: Mid to senior-level engineers working in cloud-based environments.

Microsoft Azure AI Engineer Associate

This certification focuses on designing and implementing AI and ML solutions using Azure services, such as Azure Machine Learning, Cognitive Services, and Responsible AI.

Best for: Engineers in Microsoft shops or hybrid cloud teams.

Google Professional Machine Learning Engineer

A more advanced credential than the TensorFlow cert, this certification demonstrates end-to-end ML expertise, covering problem framing, data architecture, model development, and production deployment.

Best for: Experienced engineers or those in technical leadership roles.

Data Science Certifications (General)

Certifications like the IBM Data Science Professional Certificate or Coursera/edX specializations are helpful for building foundational knowledge. They can also be good stepping stones into more advanced roles or certifications.

Best for: Early-career professionals or career switchers looking to formalize their skills. 

How to Increase Your Salary as a Machine Learning Engineer

Looking to level up your compensation? These strategies can help you stand out and command a higher paycheck:

  • Specialize: Focus on niches like natural language processing (NLP), generative AI, computer vision, or reinforcement learning, where demand is rapidly growing.

  • Master MLOps Tools: Knowing how to deploy, monitor, and manage machine learning models in production environments is a key differentiator—and often leads to higher pay.

  • Earn Cloud Certifications: Credentials from AWS, Google Cloud, or Azure show you can build and scale ML systems in the cloud, which is a must-have for many employers.

  • Contribute to Open-Source or Publish Research: Sharing your work through GitHub, blogs, or academic papers can raise your professional profile and open doors to better opportunities.

  • Build End-to-End Solutions: Being able to take a model from prototype to production shows you're not just a model builder—you’re a problem solver.

  • Target Senior or Leadership Roles: Titles like Senior ML Engineer, Applied Scientist, or AI Architect often come with significantly higher compensation. 

Conclusion

The AI/ML industry is growing at breakneck speed, and machine learning engineers are at the forefront of this industry. And, with the right skills, experience, and certifications, their earning potential is just as impressive as the tools they build. 

Whether you're just starting out or looking for ways to grow your salary, understanding average machine learning engineer salaries—and the factors that impact them—can help you succeed. Specializing, learning new tools, and getting certifications are the most effective ways to stay at the head of the pack. 

Read to learn more? Consider our AWS Machine Learning Online Training or Cloud Certification courses


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