Salary for Entry-level / Junior AI Engineer in United States during 2024

Salary details

The average entry-level / junior AI Engineer salary lies between USD 55,000 and USD 120,525 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: Nov. 17, 2024

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

The three most common job tag items assiciated with entry-level / junior AI Engineer job listings are Python, Engineering and Machine Learning. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

Python | 116 jobs Engineering | 92 jobs Machine Learning | 90 jobs LLMs | 64 jobs Computer Science | 62 jobs Research | 51 jobs Generative AI | 47 jobs Agile | 45 jobs NLP | 44 jobs Deep Learning | 42 jobs TensorFlow | 36 jobs PyTorch | 36 jobs Azure | 34 jobs Mathematics | 34 jobs Architecture | 33 jobs AWS | 28 jobs APIs | 28 jobs SQL | 25 jobs Git | 25 jobs R | 24 jobs

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

The three most common job benefits and perks assiciated with entry-level / junior AI Engineer job listings are Career development, Flex hours and Startup environment. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 85 jobs Flex hours | 34 jobs Startup environment | 24 jobs Health care | 21 jobs Competitive pay | 16 jobs Team events | 16 jobs Flex vacation | 10 jobs Fitness / gym | 7 jobs Equity / stock options | 6 jobs Conferences | 6 jobs Salary bonus | 6 jobs Gear | 5 jobs Wellness | 4 jobs Relocation support | 4 jobs Insurance | 4 jobs 401(k) matching | 3 jobs Parental leave | 3 jobs Travel | 3 jobs Medical leave | 3 jobs Transparency | 2 jobs

Salary Composition

The salary for an entry-level or junior AI Engineer in the United States typically consists of a base salary, potential bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually forms the majority of the total compensation package. Bonuses can vary significantly depending on the company and industry; tech companies and startups might offer performance-based bonuses or signing bonuses. Additional remuneration might include stock options, especially in tech startups, or comprehensive benefits packages that include health insurance, retirement plans, and other perks. The composition can also vary by region, with tech hubs like Silicon Valley or New York City offering higher base salaries to offset the higher cost of living.

Increasing Salary

To increase your salary from an entry-level position, consider gaining specialized skills or certifications that are in high demand, such as expertise in deep learning, natural language processing, or cloud computing. Pursuing a master's degree or Ph.D. in a related field can also enhance your qualifications and open up higher-paying opportunities. Networking within the industry and seeking mentorship can provide insights into career advancement. Additionally, gaining experience by working on diverse projects or contributing to open-source AI projects can make you more attractive to employers. Transitioning to a larger company or a different industry that values AI expertise, such as finance or healthcare, can also lead to higher salaries.

Educational Requirements

Most entry-level AI Engineer positions require at least a bachelor's degree in computer science, data science, mathematics, or a related field. Some employers may prefer candidates with a master's degree, especially for roles that involve more complex problem-solving or research. Coursework in machine learning, statistics, and programming languages such as Python or R is often essential. A strong foundation in mathematics, particularly linear algebra and calculus, is also important for understanding machine learning algorithms.

Helpful Certifications

While not always required, certain certifications can enhance your resume and demonstrate your commitment to the field. Certifications from recognized institutions, such as the TensorFlow Developer Certificate or the AWS Certified Machine Learning – Specialty, can be beneficial. Other valuable certifications might include those from Coursera or edX in machine learning or data science, which can provide a structured learning path and hands-on experience with real-world projects.

Required Experience

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 machine learning frameworks like TensorFlow or PyTorch, as well as familiarity with data manipulation and analysis tools, is often expected. While extensive professional experience is not required, demonstrating a portfolio of projects or contributions to open-source AI initiatives can be advantageous.

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