Salary for Senior-level / Expert Machine Learning Infrastructure Engineer during 2023

Salary details

The average senior-level / expert Machine Learning Infrastructure Engineer salary lies between USD 127,300 and USD 205,920 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: Nov. 10, 2024

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

The three most common job tag items assiciated with senior-level / expert Machine Learning Infrastructure Engineer job listings are Machine Learning, ML infrastructure and Python. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:

Machine Learning | 19 jobs ML infrastructure | 19 jobs Python | 14 jobs Pipelines | 13 jobs PyTorch | 12 jobs Engineering | 12 jobs Kubernetes | 11 jobs ML models | 10 jobs Deep Learning | 9 jobs Testing | 9 jobs AWS | 8 jobs Architecture | 8 jobs TensorFlow | 7 jobs Spark | 7 jobs Research | 7 jobs GPU | 6 jobs Model training | 6 jobs MLFlow | 6 jobs Docker | 6 jobs Computer Science | 6 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Machine Learning Infrastructure Engineer job listings are Career development, Equity / stock options and Salary bonus. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:

Career development | 16 jobs Equity / stock options | 14 jobs Salary bonus | 11 jobs Health care | 9 jobs Competitive pay | 9 jobs Startup environment | 8 jobs 401(k) matching | 6 jobs Medical leave | 6 jobs Insurance | 6 jobs Parental leave | 5 jobs Flex vacation | 5 jobs Flex hours | 4 jobs Fitness / gym | 3 jobs Team events | 3 jobs Fertility benefits | 3 jobs Wellness | 2 jobs Transparency | 2 jobs Home office stipend | 2 jobs Gear | 1 jobs Signing bonus | 1 jobs

Salary Composition

The salary for a Senior-level or Expert Machine Learning Infrastructure Engineer 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 10-20% of the salary. Additional remuneration may include stock options, especially in tech companies, and other benefits like health insurance, retirement plans, 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 lucrative stock options, while smaller companies might compensate with higher bonuses or flexible work arrangements.

Increasing Salary

To increase your salary from this position, consider pursuing leadership roles such as a Machine Learning Infrastructure Manager or Director of Machine Learning. These roles often come with higher pay and more responsibilities. 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 research can also enhance your reputation and open doors to higher-paying opportunities. Finally, negotiating your salary during performance reviews or when taking on additional responsibilities can lead to incremental increases.

Educational Requirements

Most Senior-level Machine Learning Infrastructure Engineers hold 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 require deep technical expertise and research capabilities. Advanced degrees can provide a competitive edge and are sometimes necessary for positions in cutting-edge research or at top-tier tech companies.

Helpful Certifications

While not always required, certain certifications can bolster your credentials and demonstrate expertise. Certifications such as the Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, and Microsoft Certified: Azure AI Engineer Associate are well-regarded in the industry. These certifications validate your skills in deploying machine learning models on cloud platforms, which is a critical aspect of machine learning infrastructure.

Required Experience

Typically, a Senior-level Machine Learning Infrastructure Engineer is expected to have 5-10 years of experience in the field. This experience should include a strong background in software engineering, data engineering, and machine learning model deployment. Experience with cloud platforms, distributed systems, and big data technologies is also crucial. Leadership experience, such as managing teams or projects, can be beneficial for those looking to advance further.

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