Machine Learning Infrastructure Engineer Salary in 2023

Salary details

The average Machine Learning Infrastructure Engineer salary lies between USD 132,400 and USD 205,920 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: Nov. 10, 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 2023 and the number of open jobs that where associated with them during that period:

Machine Learning | 21 jobs ML infrastructure | 21 jobs Python | 16 jobs Pipelines | 15 jobs Engineering | 13 jobs PyTorch | 12 jobs Kubernetes | 11 jobs ML models | 11 jobs Testing | 10 jobs Deep Learning | 9 jobs AWS | 9 jobs Architecture | 9 jobs TensorFlow | 7 jobs Spark | 7 jobs Research | 7 jobs GPU | 6 jobs SageMaker | 6 jobs Streaming | 6 jobs Model training | 6 jobs MLFlow | 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, Equity / stock options and Health care. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:

Career development | 18 jobs Equity / stock options | 16 jobs Health care | 11 jobs Competitive pay | 11 jobs Salary bonus | 11 jobs Startup environment | 9 jobs Medical leave | 8 jobs Insurance | 8 jobs 401(k) matching | 7 jobs Parental leave | 6 jobs Flex vacation | 5 jobs Flex hours | 4 jobs Fitness / gym | 4 jobs Team events | 4 jobs Fertility benefits | 4 jobs Wellness | 3 jobs Transparency | 2 jobs Home office stipend | 2 jobs Gear | 1 jobs Signing bonus | 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 composition can vary significantly based on the region, industry, and company size. In tech hubs like Silicon Valley, the base salary might be higher, but the cost of living is also elevated. Bonuses are often tied to individual and company performance and can range from 10% to 20% of the base salary. In larger companies, stock options or equity can form a significant part of the compensation package, providing long-term financial benefits. In contrast, smaller companies or startups might offer lower base salaries but compensate with higher equity stakes.

Increasing Salary

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

• Skill Enhancement: Continuously update your skills with the latest technologies and tools in AI/ML and infrastructure management.
• Advanced Education: Pursue advanced degrees or specialized certifications that can set you apart.
• Leadership Roles: Aim for leadership or managerial roles that come with higher responsibilities and pay.
• Industry Switch: Consider moving to industries that pay higher for ML infrastructure roles, such as finance or healthcare.
• Networking: Build a strong professional network to learn about higher-paying opportunities and negotiate better offers.

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. in a specialized area such as machine learning, data science, or systems engineering can be highly advantageous. These advanced degrees provide a deeper understanding of complex algorithms and systems, which is crucial for infrastructure roles.

Helpful Certifications

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

• 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 demonstrate your expertise in specific tools and platforms, making you more attractive to potential employers.

Required Experience

Typically, employers look for candidates with 3-5 years of experience in related fields such as software engineering, data engineering, or systems architecture. Experience with cloud platforms, containerization, and orchestration tools is often required. Practical experience in deploying and managing machine learning models in production environments is highly valued.

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