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

Salary details

The average senior-level / expert Machine Learning Infrastructure Engineer salary lies between USD 150,000 and USD 221,000 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: Nov. 17, 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 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 success, usually make up about 10-20%. Additional remuneration might include stock options, especially in tech companies, which can vary significantly depending on the company's size and success. Benefits such as health insurance, retirement contributions, and other perks also add value to the overall compensation package. Regional differences can affect these figures, with tech hubs like San Francisco and New York offering higher salaries to offset the cost of living. Similarly, larger companies or those in high-demand industries may offer more competitive packages.

Increasing Salary

To increase your salary from this position, consider pursuing leadership roles such as a Machine Learning Infrastructure Manager or Director. These roles often come with higher pay and more responsibilities. Specializing in a niche area of machine learning infrastructure, such as distributed systems or cloud-based solutions, can also make you more valuable. Additionally, gaining expertise in emerging technologies or methodologies can set you apart. Networking within the industry and building a strong professional brand through speaking engagements, publications, or contributions to open-source projects can also enhance your career prospects and salary potential.

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 or research capabilities. Advanced degrees can provide a competitive edge and are sometimes necessary for roles in cutting-edge research or at top-tier tech companies.

Helpful Certifications

While not always required, certain certifications can bolster your credentials. Certifications in cloud platforms like AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer Associate are highly regarded. Additionally, certifications in data engineering or big data, such as the Cloudera Certified Data Engineer, can be beneficial. These certifications demonstrate a commitment to staying current with industry standards and technologies.

Required Experience

Typically, a Senior-level Machine Learning Infrastructure Engineer is expected to have 5-10 years of experience in related fields. This experience should include a strong background in software engineering, data engineering, and machine learning. Experience with large-scale data systems, cloud infrastructure, and distributed computing is often essential. Leadership experience, such as managing teams or projects, can also be a significant advantage.

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