Salary for Entry-level / Junior Machine Learning Engineer in United States during 2022

Salary details

The average entry-level / junior Machine Learning Engineer salary lies between USD 108,000 and USD 140,250 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. 8, 2024

Salary trend

Top 20 Job Tags for Entry-level / Junior Machine Learning Engineer roles

The three most common job tag items assiciated with entry-level / junior Machine Learning Engineer job listings are Machine Learning, Python and Computer Science. Below you find a list of the 20 most occuring job tags in 2022 and the number of open jobs that where associated with them during that period:

Machine Learning | 54 jobs Python | 47 jobs Computer Science | 36 jobs Engineering | 35 jobs Research | 27 jobs Deep Learning | 22 jobs ML models | 18 jobs NLP | 17 jobs TensorFlow | 17 jobs PyTorch | 16 jobs Computer Vision | 15 jobs Mathematics | 15 jobs Statistics | 14 jobs Big Data | 12 jobs SQL | 12 jobs Testing | 11 jobs Pipelines | 11 jobs APIs | 11 jobs PhD | 10 jobs R | 9 jobs

Top 20 Job Perks/Benefits for Entry-level / Junior Machine Learning Engineer roles

The three most common job benefits and perks assiciated with entry-level / junior Machine Learning Engineer job listings are Career development, Startup environment and Team events. Below you find a list of the 20 most occuring job perks or benefits in 2022 and the number of open jobs that where offering them during that period:

Career development | 38 jobs Startup environment | 14 jobs Team events | 13 jobs Flex hours | 12 jobs Flex vacation | 9 jobs Conferences | 9 jobs Health care | 8 jobs Salary bonus | 8 jobs Competitive pay | 7 jobs Equity / stock options | 5 jobs Parental leave | 5 jobs Medical leave | 5 jobs Insurance | 4 jobs Wellness | 3 jobs Travel | 3 jobs Transparency | 3 jobs Flexible spending account | 3 jobs Unlimited paid time off | 2 jobs 401(k) matching | 1 jobs Lunch / meals | 1 jobs

Salary Composition

The salary for an entry-level or junior machine learning engineer in the United States typically consists of a base salary, performance bonuses, and sometimes additional remuneration such as stock options or benefits. The base salary is the fixed component and usually makes up the majority of the total compensation package. Performance bonuses can vary significantly depending on the company’s policies and the individual's performance. Additional remuneration, such as stock options, is more common in tech companies, especially startups or large tech firms, and can be a significant part of the compensation package. The composition can also vary by region, with tech hubs like Silicon Valley, Seattle, and New York offering higher salaries compared to other regions. Industry also plays a role; for instance, finance and tech industries tend to offer higher compensation compared to academia or non-profit sectors. Company size can influence the package as well, with larger companies often providing more comprehensive benefits and bonuses.

Increasing Salary

To increase your salary from an entry-level position, consider the following steps:

• Skill Enhancement: Continuously upgrade your skills by learning new programming languages, tools, and frameworks relevant to AI/ML.
• Advanced Education: Pursuing a master's degree or Ph.D. in a related field can open up higher-paying opportunities.
• Specialization: Focus on a niche area within AI/ML, such as natural language processing, computer vision, or reinforcement learning, which can command higher salaries.
• Networking: Build a strong professional network through conferences, meetups, and online platforms like LinkedIn to learn about higher-paying opportunities.
• Performance and Results: Demonstrate your impact on projects and the company’s bottom line, which can lead to promotions and salary increases.
• Certifications: Obtain relevant certifications that can validate your skills and potentially lead to higher compensation.

Educational Requirements

Most entry-level machine learning engineer positions require at least a bachelor's degree in computer science, data science, mathematics, statistics, or a related field. Some positions may prefer or require a master's degree, especially in more competitive markets or specialized roles. A strong foundation in mathematics, particularly in linear algebra, calculus, probability, and statistics, is essential. Additionally, coursework or experience in programming, data structures, algorithms, and machine learning concepts is highly beneficial.

Helpful Certifications

While not always required, certain certifications can enhance your resume and demonstrate your expertise to potential employers. Some popular certifications include:

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

These certifications can help validate your skills and knowledge in specific platforms or tools, making you a more attractive candidate.

Experience Requirements

For entry-level positions, employers typically look for candidates with some practical experience, which can be gained through internships, co-op programs, or personal projects. Experience with programming languages such as Python or R, and familiarity with machine learning libraries like TensorFlow, PyTorch, or scikit-learn, is often expected. Additionally, experience with data manipulation and analysis using tools like Pandas or SQL can be beneficial. While extensive professional experience is not required, demonstrating hands-on experience through projects or contributions to open-source projects can be advantageous.

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