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

Salary details

The average entry-level / junior Machine Learning Engineer salary lies between USD 115,200 and USD 177,100 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

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, Computer Science and Python. 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:

Machine Learning | 472 jobs Computer Science | 380 jobs Python | 360 jobs Engineering | 360 jobs Research | 212 jobs PyTorch | 195 jobs NLP | 192 jobs Deep Learning | 190 jobs TensorFlow | 180 jobs PhD | 170 jobs Statistics | 145 jobs ML models | 140 jobs E-commerce | 127 jobs LLMs | 123 jobs Computer Vision | 121 jobs Java | 121 jobs Mathematics | 109 jobs Spark | 106 jobs SQL | 100 jobs Pipelines | 99 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, Health care and Medical leave. 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 | 396 jobs Health care | 233 jobs Medical leave | 182 jobs Startup environment | 164 jobs Equity / stock options | 155 jobs Team events | 149 jobs Flex vacation | 114 jobs Parental leave | 113 jobs 401(k) matching | 106 jobs Insurance | 104 jobs Conferences | 102 jobs Flexible spending account | 92 jobs Flex hours | 90 jobs Competitive pay | 77 jobs Salary bonus | 52 jobs Transparency | 47 jobs Travel | 17 jobs Lunch / meals | 11 jobs Wellness | 10 jobs Relocation support | 8 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 equity, especially in tech companies. 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 policy and individual performance, often ranging from 5% to 15% of the base salary. Additional remuneration, like stock options, is more common in larger tech companies or startups and can be a significant part of the compensation package, especially in high-growth industries or regions like Silicon Valley.

Regional differences also play a role; for instance, salaries in tech hubs like San Francisco, New York, or Seattle tend to be higher due to the cost of living and demand for tech talent. Industry-wise, companies in finance, healthcare, and technology often offer higher compensation compared to other sectors. Company size can also influence salary composition, with larger companies typically offering 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. Specializing in high-demand areas like deep learning, natural language processing, or computer vision can make you more valuable.

• Advanced Education: Pursuing a master's degree or Ph.D. in a related field can open up higher-paying opportunities and roles with more responsibility.

• Networking and Mentorship: Building a strong professional network and seeking mentorship can provide insights into career advancement opportunities and help you navigate your career path effectively.

• Performance and Impact: Demonstrating a strong track record of performance and making a significant impact in your projects can lead to promotions and salary increases.

• Certifications: Obtaining relevant certifications 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. A strong foundation in mathematics, particularly in linear algebra, calculus, and probability, is essential. Some positions may prefer candidates with a master's degree, especially in competitive markets or for roles that require more specialized knowledge.

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
• TensorFlow Developer Certificate

These certifications can help validate your skills in specific platforms and tools, making you more attractive to employers who use these technologies.

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 programming languages like Python or R, and familiarity with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn, is often expected. Participation in hackathons, Kaggle competitions, or contributing to open-source projects can also be beneficial.

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