Salary for Mid-level / Intermediate ML Infrastructure Engineer in United States during 2024

Salary details

The average mid-level / intermediate ML Infrastructure Engineer salary lies between USD 150,000 and USD 222,000 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: Dec. 15, 2024

Top 20 Job Tags for Mid-level / Intermediate ML Infrastructure Engineer roles

The three most common job tag items assiciated with mid-level / intermediate ML Infrastructure Engineer job listings are Python, Machine Learning and ML infrastructure. 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 | 13 jobs Machine Learning | 13 jobs ML infrastructure | 13 jobs Engineering | 12 jobs Pipelines | 11 jobs DevOps | 10 jobs TensorFlow | 9 jobs PyTorch | 9 jobs GPU | 9 jobs Computer Science | 9 jobs Kubernetes | 8 jobs Linux | 8 jobs CI/CD | 8 jobs Research | 7 jobs Security | 7 jobs Testing | 7 jobs GitHub | 7 jobs Deep Learning | 6 jobs Distributed Systems | 6 jobs Airflow | 5 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate ML Infrastructure Engineer roles

The three most common job benefits and perks assiciated with mid-level / intermediate ML Infrastructure Engineer job listings are Career development, Health care and Salary bonus. 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 | 10 jobs Health care | 6 jobs Salary bonus | 6 jobs Startup environment | 4 jobs Competitive pay | 3 jobs 401(k) matching | 2 jobs Equity / stock options | 2 jobs Flex hours | 2 jobs Flex vacation | 2 jobs Fitness / gym | 2 jobs Insurance | 2 jobs Paid sabbatical | 2 jobs Transparency | 1 jobs

Salary Composition for a Mid-level ML Infrastructure Engineer

The salary for a Mid-level ML Infrastructure Engineer in the United States typically comprises several components. The fixed base salary is the largest portion, often accounting for 70-80% of the total compensation package. Bonuses, which can be performance-based or tied to company profits, usually make up about 10-15%. Additional remuneration might include stock options, especially in tech companies, and other benefits such as health insurance, retirement contributions, and paid time off. The exact composition can vary significantly depending on the region, industry, and company size. For instance, tech hubs like Silicon Valley or New York City might offer higher base salaries and stock options, while smaller companies might focus more on bonuses and flexible work arrangements.

Steps to Increase Salary from This Position

To increase your salary from a Mid-level ML Infrastructure Engineer position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging technologies and tools relevant to AI/ML and data science. Specializing in niche areas like deep learning, natural language processing, or cloud-based ML solutions 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: Transitioning into roles that involve team leadership or project management can lead to salary increases.
• Networking: Building a strong professional network can lead to opportunities in higher-paying companies or industries.
• Certifications: Obtaining advanced certifications can demonstrate your expertise and commitment to the field, potentially leading to salary negotiations.

Educational Requirements

Most Mid-level ML Infrastructure Engineer positions require at least a bachelor's degree in computer science, engineering, mathematics, or a related field. However, many employers prefer candidates with a master's degree, especially in data science, machine learning, or artificial intelligence. A strong foundation in programming, statistics, and data analysis is essential, and coursework in machine learning algorithms, data structures, and software engineering is highly beneficial.

Helpful Certifications

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

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

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

Experience Requirements

Typically, a Mid-level ML Infrastructure Engineer is expected to have 3-5 years of relevant experience. This experience should include hands-on work with machine learning models, data pipelines, and cloud-based infrastructure. Experience in software development, data engineering, and working with large datasets is also highly valued. Demonstrated experience in deploying and maintaining ML models in production environments is crucial.

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