Salary for Entry-level / Junior Machine Learning Engineer in United States during 2023

Salary details

The average entry-level / junior Machine Learning Engineer salary lies between USD 76,000 and USD 162,240 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, Python and Engineering. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:

Machine Learning | 181 jobs Python | 146 jobs Engineering | 107 jobs Computer Science | 103 jobs TensorFlow | 72 jobs PyTorch | 72 jobs Deep Learning | 70 jobs AWS | 68 jobs Research | 68 jobs ML models | 64 jobs Mathematics | 58 jobs Pipelines | 56 jobs SQL | 52 jobs Testing | 51 jobs NLP | 48 jobs Docker | 45 jobs Java | 43 jobs Consulting | 42 jobs GCP | 41 jobs Statistics | 40 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, Team events and Flex hours. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:

Career development | 120 jobs Team events | 85 jobs Flex hours | 72 jobs Startup environment | 67 jobs Competitive pay | 58 jobs Health care | 43 jobs Conferences | 43 jobs Salary bonus | 38 jobs Equity / stock options | 37 jobs Travel | 16 jobs Flex vacation | 15 jobs Insurance | 15 jobs Medical leave | 13 jobs Wellness | 12 jobs Fitness / gym | 12 jobs Parental leave | 11 jobs Relocation support | 9 jobs 401(k) matching | 6 jobs Snacks / Drinks | 6 jobs Gear | 5 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, where they are used as an incentive to align employee interests with company growth.

Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York City tend to be higher due to the cost of living and competitive job market. Industry can also influence salary composition, with finance and tech industries typically offering higher compensation packages compared to academia or non-profit sectors.

Steps to Increase Salary

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

• Skill Enhancement: Continuously update and expand your skill set. Specializing in high-demand areas such as deep learning, natural language processing, or computer vision can make you more valuable.
• Advanced Education: Pursuing a master's or Ph.D. in a related field can open up higher-paying opportunities.
• Networking: Building a strong professional network can lead to new job opportunities and insights into industry trends.
• Performance Excellence: Consistently exceeding performance expectations can lead to promotions and salary increases.
• Certifications: Obtaining relevant certifications can demonstrate your commitment to the field and enhance your qualifications.

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. However, a master's degree is often preferred and can make candidates more competitive. Coursework in machine learning, artificial intelligence, data structures, algorithms, and programming is particularly beneficial.

Helpful Certifications

While not always required, certain certifications can be advantageous:

• Google Professional Machine Learning Engineer: Validates your ability to design, build, and productionize ML models.
• AWS Certified Machine Learning – Specialty: Demonstrates expertise in using AWS services for machine learning.
• Microsoft Certified: Azure AI Engineer Associate: Focuses on using Azure services for AI solutions.
• TensorFlow Developer Certificate: Shows proficiency in using TensorFlow for building ML models.

Required Experience

For entry-level positions, employers typically look for candidates with some practical experience, which can be gained through internships, projects, or contributions to open-source ML projects. Experience with programming languages such as Python or R, familiarity with ML frameworks like TensorFlow or PyTorch, and a solid understanding of data manipulation and analysis are often expected.

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