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

Salary details

The average senior-level / expert ML Infrastructure Engineer salary lies between USD 127,000 and USD 287,500 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: 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 or Expert ML Infrastructure Engineer typically comprises several components: a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary is often the largest portion, providing a stable income. Bonuses can vary significantly depending on the company's performance and individual contributions, often ranging from 10% to 30% of the base salary. Additional remuneration, like stock options, is more common in larger tech companies or startups, where equity can be a significant part of the compensation package. Regional differences also play a role; for instance, salaries in tech hubs like Silicon Valley or New York City tend to be higher due to the cost of living and competitive job market. Industry-wise, companies in finance or specialized tech sectors might offer higher bonuses compared to traditional industries.

Increasing Salary Further

To increase your salary beyond the current position, consider pursuing leadership roles such as ML Infrastructure Manager or Director of AI/ML Engineering. These roles often come with higher compensation due to increased responsibilities. Additionally, specializing in emerging technologies or niche areas within AI/ML can make you more valuable. Networking and building a strong professional brand through speaking engagements, publications, or contributions to open-source projects can also enhance your marketability. Finally, consider negotiating your salary by leveraging offers from other companies or demonstrating your impact on the organization’s bottom line.

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 industries or academia.

Helpful Certifications

While not always mandatory, certain certifications can bolster your credentials. Certifications such as Google Cloud Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, or Microsoft Certified: Azure AI Engineer Associate are highly regarded. These certifications demonstrate proficiency in deploying and managing ML models on cloud platforms, which is a critical skill for infrastructure roles. Additionally, certifications in data engineering or DevOps can be beneficial, given the overlap in skills required for ML infrastructure.

Required Experience

Typically, a Senior-level ML Infrastructure Engineer is expected to have at least 5-10 years of experience in software engineering, data engineering, or a related field. This experience should include a strong background in building and maintaining scalable ML systems, familiarity with cloud platforms, and proficiency in programming languages such as Python, Java, or C++. Experience with ML frameworks like TensorFlow or PyTorch, as well as tools for data processing and orchestration, is also crucial.

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