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

Salary details

The average mid-level / intermediate ML Infrastructure Engineer salary lies between USD 150,000 and USD 222,000 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. 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 typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary is the fixed component and usually constitutes the majority of the total compensation package. Performance bonuses can vary significantly depending on the company's performance and individual contributions, often ranging from 10% to 20% of the base salary. Additional remuneration, such as stock options, is more common in larger tech companies or startups and can significantly increase the total compensation, especially if the company performs well. Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York tend to be higher due to the cost of living and competitive job market. Industry-wise, tech companies generally offer higher salaries compared to other sectors like finance or healthcare, although these industries are increasingly investing in AI/ML capabilities.

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 ML infrastructure, such as cloud platforms, containerization, and orchestration tools.
• Advanced Education: Pursuing a master's degree or specialized courses in AI/ML can make you more competitive for senior roles.
• Networking: Engage with professional networks and communities to learn about new opportunities and trends in the industry.
• Leadership Roles: Seek opportunities to lead projects or teams, as leadership experience can pave the way for promotions and salary increases.
• Company Switch: Sometimes, moving to a different company can result in a significant salary bump, especially if the new company values your specific skill set more highly.

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. A strong foundation in programming, data structures, algorithms, and systems design is essential. Some positions may prefer candidates with a master's degree, especially if the role involves more complex problem-solving or research-oriented tasks.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise:

• AWS Certified Machine Learning – Specialty: Validates your ability to design, implement, deploy, and maintain ML solutions on AWS.
• Google Professional Machine Learning Engineer: Demonstrates proficiency in designing, building, and productionizing ML models on Google Cloud.
• Microsoft Certified: Azure AI Engineer Associate: Focuses on using Azure AI services to build and integrate AI solutions.

Required Experience

Typically, a Mid-level ML Infrastructure Engineer is expected to have 3-5 years of experience in related roles. This experience should include hands-on work with ML models, data pipelines, and infrastructure management. Experience with cloud platforms (AWS, Google Cloud, Azure), containerization (Docker, Kubernetes), and CI/CD pipelines is often required. Familiarity with big data technologies and distributed systems can also be advantageous.

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