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

Salary details

The average senior-level / expert ML Infrastructure Engineer salary lies between USD 127,000 and USD 284,400 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. 22, 2024

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

The three most common job tag items assiciated with senior-level / expert ML Infrastructure Engineer job listings are Machine Learning, ML infrastructure and Engineering. 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:

Machine Learning | 27 jobs ML infrastructure | 27 jobs Engineering | 25 jobs Python | 23 jobs PyTorch | 18 jobs Computer Science | 18 jobs Kubernetes | 17 jobs ML models | 16 jobs TensorFlow | 13 jobs Distributed Systems | 13 jobs Pipelines | 13 jobs AWS | 12 jobs Linux | 12 jobs GPU | 11 jobs Research | 11 jobs Java | 9 jobs Scala | 8 jobs Security | 8 jobs Architecture | 8 jobs Ansible | 8 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert ML 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 2024 and the number of open jobs that where offering them during that period:

Career development | 20 jobs Equity / stock options | 15 jobs Health care | 14 jobs Flex vacation | 12 jobs Parental leave | 11 jobs Flex hours | 11 jobs Startup environment | 9 jobs Medical leave | 8 jobs Relocation support | 7 jobs Salary bonus | 7 jobs Flexible spending account | 7 jobs 401(k) matching | 6 jobs Home office stipend | 6 jobs Insurance | 5 jobs Competitive pay | 4 jobs Unlimited paid time off | 4 jobs Lunch / meals | 3 jobs Travel | 3 jobs Conferences | 3 jobs Team events | 3 jobs

Salary Composition

The salary for a Senior-level/Expert 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 10-20%. Additional remuneration might include stock options, especially in tech companies, and other benefits like 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 more substantial stock options, while smaller companies might compensate with higher bonuses or unique perks.

Increasing Salary Further

To increase your salary beyond the median of USD 192,450, consider pursuing leadership roles such as ML Infrastructure Manager or Director of Machine Learning. These positions often come with higher pay scales. Additionally, specializing in high-demand areas like AI ethics, explainable AI, or edge computing can make you more valuable. Networking within industry circles, attending conferences, and publishing in reputable journals can also enhance your profile. Finally, negotiating skills are crucial; understanding your market value and effectively communicating your contributions can lead to better compensation packages.

Educational Requirements

Most Senior-level ML Infrastructure Engineer positions 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 demand a deep understanding of machine learning algorithms and infrastructure. Advanced degrees can provide a competitive edge and are sometimes necessary for roles in research-intensive companies or industries.

Helpful Certifications

While not always mandatory, certain certifications can bolster your credentials. Certifications like the Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, and Microsoft Certified: Azure AI Engineer Associate are highly regarded. These certifications demonstrate proficiency in specific platforms and tools, which can be advantageous in roles that require expertise in cloud-based ML infrastructure.

Required Experience

Typically, a Senior-level ML Infrastructure Engineer is expected to have at least 5-10 years of experience in the field. This experience should include a strong background in software engineering, data engineering, and machine learning. Experience with cloud platforms, distributed systems, and large-scale data processing is often essential. Leadership experience, such as managing teams or projects, can also be beneficial for senior roles.

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