Salary for Senior-level / Expert Infrastructure Engineer during 2024

Salary details

The average senior-level / expert Infrastructure Engineer salary lies between USD 150,000 and USD 195,000 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: Jan. 19, 2025

Top 20 Job Tags for Senior-level / Expert Infrastructure Engineer roles

The three most common job tag items assiciated with senior-level / expert 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 | 142 jobs Engineering | 142 jobs Machine Learning | 113 jobs AWS | 104 jobs Kubernetes | 98 jobs Pipelines | 91 jobs Security | 86 jobs Architecture | 83 jobs Computer Science | 80 jobs Terraform | 77 jobs Docker | 59 jobs CI/CD | 58 jobs ML infrastructure | 57 jobs Azure | 56 jobs GCP | 52 jobs Distributed Systems | 50 jobs Java | 50 jobs DevOps | 48 jobs Spark | 44 jobs Linux | 43 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert Infrastructure Engineer roles

The three most common job benefits and perks assiciated with senior-level / expert 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 | 133 jobs Health care | 101 jobs Equity / stock options | 86 jobs Flex hours | 69 jobs Startup environment | 63 jobs Flex vacation | 59 jobs Parental leave | 57 jobs Competitive pay | 51 jobs Insurance | 51 jobs Medical leave | 43 jobs Salary bonus | 40 jobs Team events | 32 jobs 401(k) matching | 29 jobs Relocation support | 22 jobs Flexible spending account | 19 jobs Home office stipend | 18 jobs Wellness | 16 jobs Fertility benefits | 13 jobs Unlimited paid time off | 10 jobs Gear | 9 jobs

Salary Composition

The salary for a Senior-level/Expert Infrastructure Engineer in AI/ML/Data Science typically comprises a base salary, bonuses, and additional remuneration such as stock options or benefits. The base salary is often the largest component, accounting for 70-80% of the total compensation package. Bonuses, which can be performance-based or company-wide, might contribute 10-20%. Additional remuneration, including stock options, profit-sharing, or other benefits, can make up the remaining 5-10%.

Regional differences play a significant role; for instance, salaries in tech hubs like San Francisco or New York are generally higher due to the cost of living and demand for talent. Industry also impacts salary composition, with tech companies often offering more in stock options, while finance or healthcare might provide higher base salaries. Company size can influence the package as well, with larger companies typically offering more comprehensive benefits and bonuses.

Increasing Salary

To increase your salary beyond the median of USD 180,400, consider the following strategies:

• Specialization: Develop expertise in a niche area within AI/ML or data science infrastructure, such as cloud computing, big data architecture, or AI ethics. This can make you indispensable and justify a higher salary.

• Leadership Roles: Transition into managerial or leadership positions, which often come with higher pay. This might involve leading a team of engineers or overseeing large-scale projects.

• Continuous Learning: Stay updated with the latest technologies and methodologies. Attending workshops, conferences, and pursuing further education can enhance your skills and marketability.

• Networking: Build a strong professional network. Engaging with industry leaders and peers can open up opportunities for higher-paying roles.

Educational Requirements

Most senior-level infrastructure engineering roles in AI/ML/Data Science require at least a bachelor's degree in computer science, engineering, or a related field. However, a master's degree or Ph.D. is often preferred, especially for roles that involve complex problem-solving and innovation. Advanced degrees can provide a deeper understanding of algorithms, data structures, and machine learning principles, which are crucial for this field.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise:

• Certified Kubernetes Administrator (CKA): Useful for roles involving container orchestration.
• AWS Certified Solutions Architect: Beneficial for positions that require cloud infrastructure management.
• Google Professional Data Engineer: Focuses on data processing and machine learning on Google Cloud Platform.
• Microsoft Certified: Azure Solutions Architect Expert: Relevant for roles involving Azure cloud services.

Required Experience

Typically, a senior-level infrastructure engineer in AI/ML/Data Science should have 7-10 years of experience in IT infrastructure, with a significant portion of that time spent in roles related to AI/ML or data science. Experience with cloud platforms, data pipelines, and large-scale system architecture is often required. Demonstrated experience in leading projects or teams can also be crucial for securing a senior position.

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