Machine Learning Engineer Salary in United States during 2021

Salary details

The average Machine Learning Engineer salary lies between USD 125,000 and USD 256,000 in the United States. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Dec. 29, 2024

Salary trend

Salary Composition for Machine Learning Engineers

The salary for a Machine Learning Engineer in the United States typically comprises a base salary, bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary is the fixed component and usually forms the largest part of the total compensation package. Bonuses can vary significantly depending on the company's performance, individual performance, and industry standards. In tech hubs like Silicon Valley, equity or stock options are common, providing employees with a stake in the company's future success.

Regionally, salaries can vary, with higher compensation often found in tech-centric areas such as San Francisco, Seattle, and New York City. Industry also plays a role; for instance, finance and tech companies tend to offer higher salaries compared to academia or non-profit sectors. Company size can influence salary composition as well, with larger companies often providing more comprehensive benefits and bonuses compared to startups, which might offer more equity.

Steps to Increase Salary

To increase your salary as a Machine Learning Engineer, consider the following strategies:

• Skill Enhancement: Continuously update your skills with the latest technologies and methodologies in AI/ML. Specializing in high-demand areas like deep learning, natural language processing, or computer vision can make you more valuable.

• Advanced Education: Pursuing further education, such as a master's or Ph.D., can open doors to higher-level positions and salary brackets.

• Networking: Engage with professional networks and communities. Attending conferences, meetups, and workshops can lead to new opportunities and insights into industry trends.

• Leadership Roles: Transitioning into leadership or managerial roles can significantly increase your earning potential. This might involve leading a team of engineers or managing projects.

• Negotiation: When offered a new position or during performance reviews, negotiate your salary and benefits package. Research industry standards to make informed requests.

Educational Requirements

Most Machine Learning Engineer positions require at least a bachelor's degree in computer science, mathematics, statistics, or a related field. However, a master's degree or Ph.D. is often preferred, especially for roles involving complex research and development tasks. These advanced degrees provide a deeper understanding of algorithms, data structures, and statistical models, which are crucial for developing sophisticated machine learning solutions.

Helpful Certifications

While not always mandatory, certifications can enhance your resume and demonstrate your commitment to the field. Some valuable certifications include:

• Google Professional Machine Learning Engineer
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• TensorFlow Developer Certificate

These certifications validate your skills in specific platforms and tools, making you more attractive to potential employers.

Experience Requirements

Typically, employers look for candidates with at least 2-5 years of experience in machine learning or related fields. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience in deploying machine learning models in production environments is highly valued, as it demonstrates your ability to translate theoretical knowledge into practical applications.

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