Machine Learning Infrastructure Engineer Salary in 2024

Salary details

The average Machine Learning Infrastructure Engineer salary lies between USD 170,700 and USD 239,040 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

Salary trend

Top 20 Job Tags for Machine Learning Infrastructure Engineer roles

The three most common job tag items assiciated with 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 2024 and the number of open jobs that where associated with them during that period:

Machine Learning | 22 jobs ML infrastructure | 22 jobs Python | 18 jobs Pipelines | 15 jobs Kubernetes | 14 jobs Engineering | 13 jobs ML models | 13 jobs PyTorch | 11 jobs Model training | 11 jobs AWS | 10 jobs Testing | 10 jobs TensorFlow | 9 jobs Research | 7 jobs Distributed Systems | 7 jobs Security | 7 jobs Terraform | 7 jobs CI/CD | 7 jobs LLMs | 7 jobs Deep Learning | 6 jobs Computer Vision | 6 jobs

Top 20 Job Perks/Benefits for Machine Learning Infrastructure Engineer roles

The three most common job benefits and perks assiciated with Machine Learning 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 | 16 jobs Health care | 15 jobs Equity / stock options | 14 jobs Salary bonus | 10 jobs Competitive pay | 8 jobs Medical leave | 8 jobs Relocation support | 7 jobs Fertility benefits | 7 jobs Parental leave | 6 jobs Startup environment | 6 jobs Insurance | 6 jobs 401(k) matching | 5 jobs Flex hours | 4 jobs Flex vacation | 4 jobs Wellness | 4 jobs Fitness / gym | 4 jobs Gear | 2 jobs Home office stipend | 2 jobs Transparency | 1 jobs Team events | 1 jobs

Salary Composition

The salary for a Machine Learning Infrastructure Engineer typically consists of 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 makes up the majority 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 be a significant part of the total compensation, especially in regions like Silicon Valley. In smaller companies or different industries, the bonus and equity components might be less pronounced, with a greater emphasis on the base salary.

Increasing Salary

To increase your salary from the position of a Machine Learning Infrastructure Engineer, consider the following steps:

• Specialization: Develop expertise in niche areas such as distributed systems, cloud computing, or AI ethics, which are in high demand.
• Leadership Roles: Transition into roles that involve team leadership or project management, as these often come with higher pay.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML, which can make you more valuable to your employer.
• Networking: Engage with professional networks and communities to learn about new opportunities and trends in the industry.
• Negotiation Skills: Improve your negotiation skills to better advocate for higher compensation during performance reviews or when switching jobs.

Educational Requirements

Most Machine Learning 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. can be advantageous, especially for roles that demand a deeper understanding of machine learning algorithms and data science principles. Advanced degrees can also provide a competitive edge in the job market and may lead to higher starting salaries.

Helpful Certifications

While not always mandatory, certain certifications can enhance your qualifications and demonstrate your expertise to potential employers. Some valuable certifications include:

• AWS Certified Machine Learning – Specialty
• Google Professional Machine Learning Engineer
• Microsoft Certified: Azure AI Engineer Associate
• Certified Kubernetes Administrator (CKA)
• TensorFlow Developer Certificate

These certifications can validate your skills in specific technologies and platforms, making you a more attractive candidate for employers.

Required Experience

Typically, employers look for candidates with 3-5 years of experience in software engineering, data engineering, or a related field. Experience with machine learning frameworks, cloud platforms, and infrastructure automation tools is highly desirable. Additionally, hands-on experience with deploying and managing machine learning models in production environments is often required.

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