ML Infrastructure Engineer Salary in United States during 2024

Salary details

The average ML Infrastructure Engineer salary lies between USD 127,000 and USD 264,200 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. 22, 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 in the United States 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 success and individual performance, 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 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, although these industries are increasingly investing in AI/ML capabilities.

Steps to Increase Salary

To increase your salary further from the position of an ML Infrastructure Engineer, consider the following strategies:

• Specialize in High-Demand Skills: Focus on acquiring expertise in niche areas such as distributed systems, cloud computing, or AI model optimization, which are highly valued.
• Pursue Advanced Education: Obtaining a master's or Ph.D. in a relevant field can open doors to higher-level positions and salary brackets.
• Seek Leadership Roles: Transitioning into managerial or lead roles can significantly boost your earning potential.
• Negotiate Effectively: Always negotiate your salary and benefits package when joining a new company or during performance reviews.
• Network and Build a Personal Brand: Attend industry conferences, contribute to open-source projects, and publish articles to increase your visibility and reputation in the field.

Educational Requirements

Most ML Infrastructure Engineer positions require at least a bachelor's degree in computer science, engineering, or a related field. However, a master's degree is often preferred, especially for roles in leading tech companies or research-focused positions. Coursework in machine learning, data structures, algorithms, and systems design is particularly beneficial. Some roles may also require knowledge of cloud platforms and distributed computing.

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)

These certifications validate your skills in cloud platforms and machine learning, which are crucial for ML infrastructure roles.

Required Experience

Typically, employers look for candidates with 3-5 years of experience in software engineering, data engineering, or a related field. Experience with cloud platforms (AWS, Google Cloud, Azure), containerization (Docker, Kubernetes), and infrastructure as code (Terraform, Ansible) is often required. Familiarity with machine learning frameworks like TensorFlow or PyTorch and experience in deploying and scaling ML models in production environments are also highly valued.

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