Salary for Mid-level / Intermediate Machine Learning Quality Engineer during 2024

Salary details

The average mid-level / intermediate Machine Learning Quality Engineer salary lies between USD 121,600 and USD 188,400 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.

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 Composition for a Mid-level Machine Learning Quality Engineer

The salary for a Mid-level Machine Learning Quality Engineer typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary is often the largest component, making up about 70-80% of the total compensation package. Performance bonuses can vary significantly, ranging from 10-20% of the base salary, depending on the company's performance and individual contributions. Additional remuneration, such as stock options, profit-sharing, or benefits like health insurance and retirement plans, can account for the remaining 5-10%.

Regional differences play a significant role in salary composition. For instance, tech hubs like Silicon Valley or New York City may 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 regions may offer lower base salaries but compensate with more substantial bonuses or benefits. Industry and company size also influence salary composition; larger tech companies or those in high-demand sectors like finance or healthcare may offer more competitive packages compared to smaller startups or companies in less lucrative industries.

Steps to Increase Salary from This Position

To increase your salary from a Mid-level Machine Learning Quality Engineer position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging AI/ML technologies and tools. Specializing in niche 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 doors to higher-level positions and salary brackets.
• Leadership Roles: Seek opportunities to lead projects or teams, which can demonstrate your capability for higher responsibility roles.
• Networking: Build a strong professional network within the AI/ML community. Attend conferences, workshops, and seminars to connect with industry leaders and peers.
• Performance Excellence: Consistently exceed performance expectations and document your achievements to leverage during salary negotiations or performance reviews.

Educational Requirements

Most Mid-level Machine Learning Quality Engineer positions require at least a bachelor's degree in computer science, data science, engineering, or a related field. However, a master's degree is often preferred, especially in competitive markets. Coursework in machine learning, statistics, data analysis, and software engineering is crucial. Some roles may also require knowledge of specific programming languages like Python, R, or Java, and familiarity with machine learning frameworks such as TensorFlow or PyTorch.

Helpful Certifications

While not always mandatory, certain certifications can enhance your qualifications and demonstrate your commitment to the field. Some valuable certifications include:

• Certified Machine Learning Professional (CMLP)
• TensorFlow Developer Certificate
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Google Professional Machine Learning Engineer

These certifications can validate your skills and knowledge, making you a more attractive candidate to potential employers.

Required Experience

Typically, a Mid-level Machine Learning Quality Engineer is expected to have 3-5 years of relevant experience. This experience should include hands-on work with machine learning models, data analysis, and quality assurance processes. Experience in software development, testing, and debugging is also beneficial. Familiarity with agile methodologies and experience working in cross-functional teams can be advantageous.

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