Machine learning (ML) engineers research, design, and develop critical artificial intelligence on data science teams. Whether they’re working on algorithms to power your Spotify recommendations or creating algorithms to make real-time stock predictions, machine learning engineers are in high demand in many industries. As a result, they're well-compensated for their in-depth knowledge and ability to make decisions that drive profits.

In this article, we'll discuss what machine learning engineers do and the average salaries by experience and location. Afterward, if you're interested in pursuing a career in machine learning, consider enrolling in the AI Foundations for Everyone Specialization to learn how to build, deploy, and innovate with advanced techniques and real-world machine learning projects.

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AI Foundations for Everyone

Unlock your potential with AI. Build job-ready AI skills to enhance your career. Everyone welcome. No prior AI background required.

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What is the salary of a machine learning engineer? 

The average salary of a machine learning engineer is impacted by many factors, including experience, industry, and geographic location. However, according to various salary aggregate sites, the average US salary for a machine learning engineer ranges from $116,416 to $140,180, which is significantly more than the median salary in the US [1, 2, 3, 4, 5]. 

Machine learning engineer salaries by experience

Experience has a big impact on what machine learning engineers can expect to make. Generally, the more experience a machine learning engineer has, the more they can expect to make in their role. According to Glassdoor, the base pay by experience breaks down as follows [5]:

• 0–1 years: $98,945 

• 1–3 years: $112,424

• 4–6 years: $123,128

• 7–9 years: $134,037

• 10–14 years: $154,016

• 15+ years: $167,496

Those in more senior positions can typically expect to make even more. For example, according to Glassdoor, the average annual base salary that a senior machine learning engineer makes is $156,993 across all years of experience [6].

What is a starting senior machine learning engineer salary?

Senior machine learning engineers start out with an average base salary of $131,568. With experience, this raises significantly–senior machine learning engineers with over 15 years of experience typically bring in around $177,416 at baseline [6].

Master advanced machine learning techniques with the Machine Learning Specialization. Through hands-on projects, you'll explore core techniques, including how to build ML models with NumPy & sci-kit-learn, build & train supervised models for prediction and binary classification tasks (linear, logistic regression), apply best practices for ML development, and more.

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Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

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Algorithms, Artificial Neural Network, Mathematics, Human Learning, Linear Regression, Machine Learning, Artificial Neural Networks, Deep Learning, Critical Thinking, Recommender Systems, Network Model, Regression, Decision Trees, Applied Machine Learning, Machine Learning Algorithms, Logistic Regression, Python Programming, Advice for Model Development, Tensorflow, Tree Ensembles, Xgboost, Supervised Learning, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, Collaborative Filtering, Anomaly Detection, Reinforcement Learning, Unsupervised Learning

Machine learning engineer industries 

As data becomes increasingly more valuable, so too do machine learning engineers capable of manipulating it with artificial intelligence. Applying your expertise in an area of rapid growth may lead to more job opportunities and advancement. Consider the five top innovations in machine learning, according to AI magazine [7]: 

• Deep learning

• Improved cybersecurity

• No-code AI

• Machine learning operationalization management

• Hyperautomation

Salaries for related jobs in machine learning

Machine learning engineering isn’t the only game in town for those with the required skill set. According to Glassdoor, here’s how other similar jobs’ base salaries stack up: 

• Software engineer: Machine learning: $150,134 [8]

• Research engineer: $135,655 [9]

• Machine learning research scientist: $154,286 [10]

• Machine learning scientist: $132,339 [11]

Machine learning engineer pay by location 

Typically, salaries vary from region to region. Every location has its own cost of living and market competition, which inevitably impacts the salary that a machine learning engineer can expect to make. 

Here are the average base salaries for machine learning engineers across the country—from the West Coast to the East, the Midwest to the South—courtesy of Glassdoor: 

Job outlook for machine learning engineers 

The job outlook for machine learning engineers is positive. According to the Bureau of Labor Statistics, computer and information research scientists – the group under which machine learning engineers typically fall – are projected to grow by 26 percent between 2023 and 2033 [12]. 

In 2023, meanwhile, Indeed ranked machine learning engineers as one of the top 10 jobs in the United States, noting its high salary and the 53 percent job growth seen between 2020 and 2023 as key reasons [13]. 

Learn about machine learning with Coursera

Becoming a machine learning engineer takes time and dedication. To get started, consider taking Stanford and DeepLearning.AI's Machine Learning Specialization. Over three courses, you can explore foundational machine learning concepts and develop practical skills, including how to build and train a neural network with TensorFlow to perform multi-class classification.

Looking to advance your machine learning skills? DeepLearning.AI’s Deep Learning Specialization is a five-course program designed for intermediate learners who want to build and train deep neural networks, identify key architecture parameters, and implement vectorized neural networks and deep learning to applications. Plus, with this Specialization, you can train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow.

specialization

Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

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(32,368 ratings)

603,139 already enrolled

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Algorithms, Artificial Neural Network, Mathematics, Human Learning, Linear Regression, Machine Learning, Artificial Neural Networks, Deep Learning, Critical Thinking, Recommender Systems, Network Model, Regression, Decision Trees, Applied Machine Learning, Machine Learning Algorithms, Logistic Regression, Python Programming, Advice for Model Development, Tensorflow, Tree Ensembles, Xgboost, Supervised Learning, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, Collaborative Filtering, Anomaly Detection, Reinforcement Learning, Unsupervised Learning

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Deep Learning

Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques!

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Algorithms, Artificial Neural Network, Transformers, Data Analysis, Recurrent Neural Network, Computer Programming, Mathematics, Human Learning, Convolutional Neural Network, Calculus, Machine Learning, Network Architecture, Organizational Development, Artificial Neural Networks, Deep Learning, Tensorflow, Network Model, Regression, Computer Vision, Mathematical Theory & Analysis, Applied Machine Learning, Strategy, Machine Learning Algorithms, Linear Algebra, Python Programming, Neural Network Architecture, Backpropagation, Object Detection and Segmentation, Facial Recognition System, Mathematical Optimization, hyperparameter tuning, Multi-Task Learning, Decision-Making, Inductive Transfer, Natural Language Processing, Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Attention Models