MLOps Engineer Salary in United States during 2023

Salary details

The average MLOps Engineer salary lies between USD 124,000 and USD 160,000 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: Nov. 17, 2024

Salary trend

Top 20 Job Tags for MLOps Engineer roles

The three most common job tag items assiciated with MLOps Engineer job listings are MLOps, Machine Learning 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:

MLOps | 103 jobs Machine Learning | 102 jobs Python | 85 jobs Engineering | 74 jobs Pipelines | 73 jobs Kubernetes | 65 jobs AWS | 57 jobs CI/CD | 56 jobs Docker | 55 jobs ML models | 52 jobs DevOps | 52 jobs TensorFlow | 47 jobs Testing | 42 jobs Azure | 42 jobs Computer Science | 42 jobs PyTorch | 41 jobs GCP | 41 jobs MLFlow | 39 jobs Terraform | 36 jobs Kubeflow | 32 jobs

Top 20 Job Perks/Benefits for MLOps Engineer roles

The three most common job benefits and perks assiciated with MLOps Engineer job listings are Career development, Flex hours 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 | 73 jobs Flex hours | 35 jobs Health care | 35 jobs Flex vacation | 34 jobs Parental leave | 25 jobs Startup environment | 23 jobs Team events | 23 jobs Equity / stock options | 22 jobs Competitive pay | 20 jobs Salary bonus | 15 jobs Medical leave | 14 jobs Conferences | 10 jobs Insurance | 10 jobs 401(k) matching | 7 jobs Unlimited paid time off | 7 jobs Transparency | 5 jobs Relocation support | 5 jobs Pet friendly | 5 jobs Home office stipend | 5 jobs Gear | 4 jobs

Salary Composition for MLOps Engineers

The salary composition for MLOps Engineers in the United States typically includes 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. Bonuses can vary significantly depending on the company's performance and individual contributions, 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 be a significant part of the total compensation, especially in high-growth industries.

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 markets. Industry-wise, tech companies, financial services, and healthcare tend to offer higher salaries compared to other sectors. Company size can also influence salary composition, with larger companies often providing more comprehensive benefits and bonuses.

Steps to Increase Salary

To increase your salary as an MLOps Engineer, consider the following strategies:

• Skill Enhancement: Continuously update your skills in the latest AI/ML tools and technologies. Specializing in high-demand areas like cloud computing, container orchestration, or data engineering can make you more valuable.

• Advanced Education: Pursuing a master's degree or relevant certifications can enhance your qualifications and open up higher-paying opportunities.

• Networking: Engage with professional communities and attend industry conferences to expand your network. This can lead to new job opportunities or promotions within your current organization.

• Performance and Negotiation: Consistently demonstrate your value through successful project completions and seek feedback for improvement. When the time is right, negotiate for a raise based on your contributions and market research.

Educational Requirements

Most MLOps Engineer positions require at least a bachelor's degree in computer science, engineering, data science, or a related field. A strong foundation in programming, data structures, and algorithms is essential. Some employers may prefer candidates with a master's degree, especially for more senior roles, as it indicates a deeper understanding of machine learning and data management principles.

Helpful Certifications

Certifications can bolster your credentials and demonstrate expertise in specific areas. Some valuable certifications for MLOps Engineers 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 validate your skills in cloud platforms, machine learning frameworks, and container orchestration, which are crucial for MLOps roles.

Required Experience

Typically, MLOps Engineer roles require 3-5 years of experience in related fields such as software engineering, data engineering, or DevOps. Experience with machine learning models, data pipelines, and cloud infrastructure is highly valued. Hands-on experience with tools like Docker, Kubernetes, and CI/CD pipelines is often a prerequisite.

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