Infrastructure Engineer Salary in United States during 2024

Salary details

The average Infrastructure Engineer salary lies between USD 150,000 and USD 195,000 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: Jan. 19, 2025

Top 20 Job Tags for Infrastructure Engineer roles

The three most common job tag items assiciated with Infrastructure Engineer job listings are Python, Engineering and Machine Learning. 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:

Python | 249 jobs Engineering | 235 jobs Machine Learning | 190 jobs Kubernetes | 171 jobs Pipelines | 159 jobs AWS | 158 jobs Security | 146 jobs Architecture | 134 jobs Computer Science | 120 jobs Terraform | 117 jobs Docker | 105 jobs CI/CD | 100 jobs ML infrastructure | 92 jobs Azure | 90 jobs DevOps | 90 jobs GCP | 86 jobs Java | 82 jobs Linux | 81 jobs Testing | 74 jobs Spark | 72 jobs

Top 20 Job Perks/Benefits for Infrastructure Engineer roles

The three most common job benefits and perks assiciated with Infrastructure Engineer job listings are Career development, Health care and Equity / stock options. 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 | 219 jobs Health care | 160 jobs Equity / stock options | 135 jobs Startup environment | 98 jobs Competitive pay | 93 jobs Flex hours | 92 jobs Flex vacation | 86 jobs Insurance | 80 jobs Parental leave | 79 jobs Salary bonus | 68 jobs Medical leave | 67 jobs Team events | 52 jobs 401(k) matching | 45 jobs Relocation support | 36 jobs Wellness | 27 jobs Home office stipend | 27 jobs Flexible spending account | 23 jobs Unlimited paid time off | 21 jobs Fertility benefits | 21 jobs Gear | 15 jobs

Salary Composition

In the United States, the salary composition for an Infrastructure Engineer specializing in AI/ML/Data Science can vary significantly based on factors such as region, industry, and company size. Typically, the salary is divided into three main components:

• Base Salary: This is the fixed annual amount and usually constitutes the largest portion of the total compensation package. In tech hubs like Silicon Valley or New York City, the base salary might be higher due to the cost of living and competitive job market.

• Bonus: Bonuses can be performance-based or company-wide and are often tied to individual, team, or company performance metrics. In larger tech companies, bonuses can be a significant part of the compensation, sometimes ranging from 10% to 20% of the base salary.

• Additional Remuneration: This includes stock options, equity, or restricted stock units (RSUs), which are common in tech companies, especially startups. Benefits such as health insurance, retirement plans, and other perks also fall under this category.

Increasing Salary

To increase your salary from this position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging technologies and tools relevant to AI/ML and data infrastructure. Specializing in niche areas can make you more valuable.

• Advanced Education: Pursuing a master's degree or Ph.D. in a related field can open up higher-level positions and salary brackets.

• Leadership Roles: Transitioning into managerial or leadership roles can significantly boost your earning potential. This might involve leading a team of engineers or managing large-scale projects.

• Networking: Building a strong professional network can lead to opportunities in higher-paying companies or roles. Attend industry conferences, join professional groups, and engage in online forums.

Educational Requirements

Most positions for an Infrastructure Engineer in AI/ML/Data Science require at least a bachelor's degree in computer science, engineering, information technology, or a related field. However, many employers prefer candidates with a master's degree or higher, especially for roles that involve complex problem-solving and strategic planning.

Helpful Certifications

Certifications can enhance your resume and demonstrate your expertise. Some valuable certifications include:

• Certified Kubernetes Administrator (CKA): Useful for managing containerized applications.
• AWS Certified Solutions Architect: Demonstrates proficiency in cloud infrastructure, which is crucial for AI/ML applications.
• Google Professional Data Engineer: Focuses on data processing and machine learning models.
• Microsoft Certified: Azure AI Engineer Associate: Validates skills in AI solutions on Azure.

Required Experience

Typically, employers look for candidates with 3-5 years of experience in infrastructure engineering or a related field. Experience with cloud platforms, data pipelines, and large-scale system architecture is often required. Familiarity with AI/ML frameworks and tools is also beneficial.

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