ML Infrastructure Engineer Salary in 2024

Salary details

The average ML Infrastructure Engineer salary lies between USD 143,100 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. 15, 2024

Top 20 Job Tags for ML Infrastructure Engineer roles

The three most common job tag items assiciated with 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 | 45 jobs ML infrastructure | 45 jobs Engineering | 41 jobs Python | 40 jobs PyTorch | 29 jobs Pipelines | 29 jobs Kubernetes | 28 jobs Computer Science | 28 jobs TensorFlow | 24 jobs ML models | 23 jobs GPU | 22 jobs Research | 22 jobs Distributed Systems | 21 jobs Linux | 20 jobs Security | 16 jobs AWS | 15 jobs Architecture | 14 jobs Model training | 14 jobs CI/CD | 14 jobs Testing | 13 jobs

Top 20 Job Perks/Benefits for ML Infrastructure Engineer roles

The three most common job benefits and perks assiciated with ML Infrastructure Engineer job listings are Career development, Health care and Equity / stock options. 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 | 35 jobs Health care | 22 jobs Equity / stock options | 18 jobs Flex vacation | 14 jobs Startup environment | 14 jobs Flex hours | 13 jobs Salary bonus | 13 jobs Parental leave | 11 jobs 401(k) matching | 9 jobs Relocation support | 8 jobs Medical leave | 8 jobs Insurance | 8 jobs Competitive pay | 7 jobs Flexible spending account | 7 jobs Home office stipend | 6 jobs Unlimited paid time off | 5 jobs Paid sabbatical | 5 jobs Lunch / meals | 3 jobs Travel | 3 jobs Fitness / gym | 3 jobs

Salary Composition for ML Infrastructure Engineer

The salary for an 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 forms the largest part of the total compensation package. Performance bonuses can vary significantly depending on the company's success and individual performance, often ranging from 10% to 20% of the base salary. Additional remuneration, like 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 Silicon Valley or New York City 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.

Steps to Increase Salary

To increase your salary from the position of an ML Infrastructure Engineer, 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 valuable to employers.
• Leadership Roles: Aim for leadership or managerial roles within your team, which often come with higher pay.
• Networking: Build a strong professional network to learn about higher-paying opportunities and gain insights into industry trends.
• Certifications: Obtain relevant certifications that can validate your expertise and potentially lead to salary negotiations.

Educational Requirements

Most ML Infrastructure Engineer positions require at least a bachelor's degree in computer science, engineering, or a related field. A strong foundation in mathematics, statistics, and programming is essential. Many employers prefer candidates with a master's degree or higher, especially for senior roles. Courses in machine learning, data structures, algorithms, and distributed systems are particularly beneficial.

Helpful Certifications

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

• AWS Certified Machine Learning – Specialty: Validates expertise in building, training, tuning, and deploying ML models on AWS.
• Google Professional Machine Learning Engineer: Demonstrates proficiency in designing, building, and productionizing ML models.
• Certified Kubernetes Administrator (CKA): Useful for roles involving container orchestration.
• TensorFlow Developer Certificate: Shows proficiency in using TensorFlow for ML tasks.

Required Experience

Typically, employers look for candidates with 3-5 years of experience in software engineering or a related field, with a focus on machine learning infrastructure. Experience with cloud platforms (AWS, Google Cloud, Azure), containerization (Docker), and orchestration tools (Kubernetes) is often required. Familiarity with CI/CD pipelines and experience in deploying ML models in production environments are also highly valued.

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