Machine Learning Infrastructure Engineer Salary in United States during 2023

Salary details

The average Machine Learning Infrastructure Engineer salary lies between USD 150,000 and USD 220,000 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. 10, 2024

Salary trend

Top 20 Job Tags for Machine Learning Infrastructure Engineer roles

The three most common job tag items assiciated with Machine Learning Infrastructure Engineer job listings are Machine Learning, ML infrastructure and Python. 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 | 21 jobs ML infrastructure | 21 jobs Python | 16 jobs Pipelines | 15 jobs Engineering | 13 jobs PyTorch | 12 jobs Kubernetes | 11 jobs ML models | 11 jobs Testing | 10 jobs Deep Learning | 9 jobs AWS | 9 jobs Architecture | 9 jobs TensorFlow | 7 jobs Spark | 7 jobs Research | 7 jobs GPU | 6 jobs SageMaker | 6 jobs Streaming | 6 jobs Model training | 6 jobs MLFlow | 6 jobs

Top 20 Job Perks/Benefits for Machine Learning Infrastructure Engineer roles

The three most common job benefits and perks assiciated with Machine Learning Infrastructure Engineer job listings are Career development, Equity / stock options and Health care. 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 | 18 jobs Equity / stock options | 16 jobs Health care | 11 jobs Competitive pay | 11 jobs Salary bonus | 11 jobs Startup environment | 9 jobs Medical leave | 8 jobs Insurance | 8 jobs 401(k) matching | 7 jobs Parental leave | 6 jobs Flex vacation | 5 jobs Flex hours | 4 jobs Fitness / gym | 4 jobs Team events | 4 jobs Fertility benefits | 4 jobs Wellness | 3 jobs Transparency | 2 jobs Home office stipend | 2 jobs Gear | 1 jobs Signing bonus | 1 jobs

Salary Composition for Machine Learning Infrastructure Engineers

The salary for a Machine Learning Infrastructure Engineer in the United States typically comprises a base salary, bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually forms the largest part of the total compensation package. Bonuses can vary significantly depending on the company's performance, individual performance, and industry standards. In tech hubs like Silicon Valley, bonuses and stock options can be substantial, often making up a significant portion of the total compensation. In contrast, companies in regions with a lower cost of living might offer smaller bonuses but a competitive base salary. Larger companies or those in high-demand industries like finance or tech often provide more lucrative compensation packages compared to smaller firms or those in less competitive sectors.

Steps to Increase Salary from This Position

To increase your salary from the position of a Machine Learning Infrastructure Engineer, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging technologies and tools relevant to machine learning infrastructure, such as cloud platforms, containerization, and orchestration tools.
• Advanced Education: Pursuing advanced degrees or specialized certifications can make you more competitive and justify a higher salary.
• Leadership Roles: Transitioning into leadership or managerial roles can significantly increase your earning potential.
• Industry Shift: Moving to a higher-paying industry, such as finance or healthcare, where machine learning is critical, can also boost your salary.
• Networking and Visibility: Building a strong professional network and increasing your visibility in the field through speaking engagements or publications can open up higher-paying opportunities.

Educational Requirements

Most Machine Learning Infrastructure Engineer positions require at least a bachelor's degree in computer science, engineering, or a related field. However, a master's degree or Ph.D. can be advantageous, especially for roles that require a deep understanding of machine learning algorithms and data architecture. Courses in data structures, algorithms, distributed systems, and machine learning are particularly relevant.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise to potential employers. Some valuable certifications include:

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

These certifications validate your skills in specific tools and platforms that are widely used in the industry.

Required Experience

Typically, employers look for candidates with 3-5 years of experience in software engineering, data engineering, or a related field. Experience with cloud platforms, distributed systems, and machine learning frameworks is often required. Practical experience in deploying and managing machine learning models in production environments is highly valued.

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