Machine Learning Infrastructure Engineer Salary in United States during 2024

Salary details

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

The salary for a Machine Learning Infrastructure Engineer in the United States typically comprises a base salary, bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually forms the largest part of the total compensation package. Bonuses can vary significantly depending on the company's performance, individual performance, and industry standards. In tech hubs like Silicon Valley, bonuses and stock options can be substantial, reflecting the high demand for skilled professionals. In contrast, regions with a lower cost of living might offer smaller bonuses but still provide competitive base salaries. Larger companies often provide more comprehensive benefits packages, including health insurance, retirement plans, and professional development opportunities, which can add significant value to the overall compensation.

Steps to Increase Salary from This Position

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

• Skill Enhancement: Continuously update your skills in emerging technologies and tools relevant to AI/ML infrastructure, such as cloud platforms, containerization, and orchestration tools.
• Advanced Education: Pursuing advanced degrees or specialized certifications can make you more valuable to employers.
• Leadership Roles: Seek opportunities to lead projects or teams, as managerial roles often come with higher pay.
• Networking: Build a strong professional network to learn about higher-paying opportunities and industry trends.
• Industry Transition: Consider moving to industries that pay higher salaries for ML infrastructure roles, such as finance or healthcare.

Educational Requirements

Most Machine Learning Infrastructure Engineer positions require at least a bachelor's degree in computer science, engineering, or a related field. However, a master's degree or Ph.D. can be advantageous, especially for roles that involve complex problem-solving and research. Coursework in machine learning, data structures, algorithms, and systems design is particularly relevant. Some positions may also value interdisciplinary knowledge, such as a combination of computer science and statistics.

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 tools and platforms commonly used in machine learning infrastructure.

Experience Requirements

Typically, employers look for candidates with several years of experience in software engineering, data engineering, or a related field. Experience with cloud platforms, distributed systems, and large-scale data processing is often required. Familiarity with machine learning frameworks and tools, such as TensorFlow, PyTorch, or Apache Spark, is also important. Practical experience in deploying and managing machine learning models in production environments is highly valued.

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