Salary for Senior-level / Expert Infrastructure Engineer in United States during 2024

Salary details

The average senior-level / expert 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 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

In the United States, the salary composition for a Senior-level/Expert Infrastructure Engineer in AI/ML/Data Science typically includes a combination of a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the majority of the total compensation package, ranging from 60% to 80%. Bonuses can vary significantly depending on the company’s performance and individual contributions, usually accounting for 10% to 20% of the total compensation. Additional remuneration, such as stock options, can make up the remaining 10% to 20%, particularly in larger tech firms or startups. The exact composition can vary based on the region, with tech hubs like Silicon Valley offering higher equity components, and industry, with finance and healthcare sectors sometimes offering higher bonuses. Company size also plays a role, with larger companies often providing more structured bonus and equity packages.

Increasing Salary Further

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

• Specialization: Develop expertise in niche areas within AI/ML or data science, such as natural language processing, computer vision, or AI ethics, which are in high demand.
• Leadership Roles: Transition into leadership or managerial roles, such as a Director of Infrastructure or Chief Technology Officer, which typically offer higher compensation.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML, and consider pursuing advanced certifications or degrees.
• Networking: Build a strong professional network to learn about higher-paying opportunities and gain insights into industry trends.
• Negotiation Skills: Enhance your negotiation skills to better advocate for higher compensation during job offers or performance reviews.

Educational Requirements

For a Senior-level/Expert Infrastructure Engineer role in AI/ML/Data Science, most employers require at least a bachelor’s degree in computer science, engineering, mathematics, or a related field. However, a master’s degree or Ph.D. is often preferred, especially for roles that involve complex problem-solving and research. Advanced degrees can provide a deeper understanding of AI/ML concepts and methodologies, making candidates more competitive for senior positions.

Helpful Certifications

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

• Certified Machine Learning Professional (CMLP)
• Google Professional Machine Learning Engineer
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Data Science Council of America (DASCA) Senior Data Scientist

These certifications can validate your skills in specific platforms and tools, making you more attractive to employers.

Required Experience

Typically, a Senior-level/Expert Infrastructure Engineer in AI/ML/Data Science is expected to have at least 5 to 10 years of relevant experience. This experience should include a strong background in infrastructure engineering, with a focus on AI/ML systems. Experience in designing, deploying, and managing scalable infrastructure solutions, as well as proficiency in programming languages such as Python, Java, or C++, is often required. Additionally, experience with cloud platforms like AWS, Azure, or Google Cloud is highly valued.

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