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

πŸ’° The median Salary for Entry-level / Junior Machine Learning Engineer during 2024 is USD 139,650

✏️ This salary info is based on 284 individual salaries reported during 2024

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Salary details

The average entry-level / junior Machine Learning Engineer salary lies between USD 113,500 and USD 177,100 globally. 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.

Job title
Machine Learning Engineer
Experience
Entry-level / Junior
Region
global/worldwide
Salary year
2024
Sample size
284
Top 10%
$ 250,000
Top 25%
$ 177,100
Median
$ 139,650
Bottom 25%
$ 113,500
Bottom 10%
$ 104,500

All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated:

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 typically consists of a base salary, performance bonuses, and 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 might include stock options, especially in tech companies, and benefits like health insurance, retirement plans, and paid time off.

The composition can vary by region, industry, and company size. For instance, tech hubs like Silicon Valley or New York City might offer higher base salaries and stock options due to the high cost of living and competitive job market. In contrast, companies in smaller cities or less tech-centric industries might offer lower base salaries but compensate with other benefits. Larger companies often have more structured compensation packages, while startups might offer more equity to attract talent.

Increasing Salary

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

  • Skill Development: Continuously improve your technical skills, especially in high-demand areas like deep learning, natural language processing, or computer vision. Mastering popular tools and frameworks such as TensorFlow, PyTorch, or scikit-learn 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: Building a strong professional network can lead to new job opportunities and insights into industry trends. Attend conferences, join professional groups, and participate in online forums.

  • Certifications: Obtaining relevant certifications can demonstrate your expertise and commitment to the field, potentially leading to salary increases.

  • Experience and Projects: Gain experience by working on diverse projects, contributing to open-source projects, or participating in hackathons. This can enhance your resume and make you more attractive to employers.

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 with more complex responsibilities.

Helpful Certifications

While not always required, certain certifications can be beneficial:

  • Google Professional Machine Learning Engineer: Validates your ability to design, build, and productionize ML models on Google Cloud.

  • AWS Certified Machine Learning – Specialty: Demonstrates expertise in building, training, tuning, and deploying machine learning models on AWS.

  • Microsoft Certified: Azure AI Engineer Associate: Focuses on using Azure services to build and deploy AI solutions.

  • TensorFlow Developer Certificate: Shows proficiency in using TensorFlow to build and train models.

These certifications can help differentiate you from other candidates and show your commitment to the field.

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 and libraries, is often expected. Demonstrating experience through a portfolio of projects or contributions to open-source projects can be advantageous.

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