Salary for Senior-level / Expert MLOps Engineer in United States during 2023

Salary details

The average senior-level / expert MLOps Engineer salary lies between USD 140,000 and USD 199,000 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: Dec. 22, 2024

Salary trend

Top 20 Job Tags for Senior-level / Expert MLOps Engineer roles

The three most common job tag items assiciated with senior-level / expert MLOps Engineer job listings are Machine Learning, MLOps 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 | 68 jobs MLOps | 68 jobs Python | 56 jobs Pipelines | 52 jobs Engineering | 49 jobs Kubernetes | 47 jobs Docker | 42 jobs AWS | 39 jobs DevOps | 35 jobs ML models | 34 jobs GCP | 31 jobs CI/CD | 31 jobs Azure | 28 jobs TensorFlow | 27 jobs MLFlow | 27 jobs Spark | 25 jobs Testing | 25 jobs Terraform | 25 jobs Computer Science | 24 jobs Kubeflow | 23 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert MLOps Engineer roles

The three most common job benefits and perks assiciated with senior-level / expert MLOps Engineer job listings are Career development, Flex hours and Flex vacation. 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 | 43 jobs Flex hours | 25 jobs Flex vacation | 24 jobs Health care | 21 jobs Equity / stock options | 19 jobs Parental leave | 17 jobs Team events | 15 jobs Competitive pay | 14 jobs Salary bonus | 13 jobs Startup environment | 11 jobs Medical leave | 10 jobs Insurance | 8 jobs 401(k) matching | 6 jobs Conferences | 6 jobs Home office stipend | 5 jobs Unlimited paid time off | 5 jobs Transparency | 4 jobs Lunch / meals | 3 jobs Fitness / gym | 3 jobs Relocation support | 3 jobs

Salary Composition for Senior MLOps Engineer Roles

The salary for a Senior MLOps 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 often the largest component, accounting for approximately 70-80% of the total compensation package. Performance bonuses can vary significantly, ranging from 10-20% of the base salary, depending on the company's performance and individual contributions. Additional remuneration, such as stock options, is more common in larger tech companies and startups, where it can form a significant part of the total compensation, sometimes up to 20-30%. The composition can vary by region, with tech hubs like Silicon Valley offering higher base salaries and equity options compared to other regions. Industry also plays a role; for instance, finance and healthcare sectors might offer higher bonuses, while tech companies might provide more in stock options. Company size can influence the package, with larger companies often providing more structured bonuses and equity.

Steps to Increase Salary from a Senior MLOps Engineer Position

To increase your salary from a Senior MLOps Engineer position, consider the following strategies:

• Specialize in Niche Areas: Developing expertise in niche areas such as AI ethics, edge computing, or advanced automation can make you more valuable.
• Pursue Leadership Roles: Transitioning into managerial or leadership roles can significantly increase your earning potential.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML and MLOps. Advanced certifications or courses can enhance your skills.
• Network and Build Industry Connections: Engaging with industry professionals can open up opportunities for higher-paying roles.
• Negotiate Effectively: When offered a new position or during performance reviews, negotiate for higher pay based on your contributions and market research.

Educational Requirements for Senior MLOps Engineer Roles

Most Senior MLOps Engineer positions require at least a bachelor's degree in computer science, engineering, data science, or a related field. However, a master's degree or Ph.D. can be advantageous, especially for roles in research-intensive industries or companies. Advanced degrees often provide a deeper understanding of machine learning algorithms, data structures, and software engineering principles, which are crucial for MLOps roles.

Helpful Certifications for MLOps Engineers

Certifications can enhance your credibility and demonstrate your commitment to the field. Some valuable certifications include:

• Certified Kubernetes Administrator (CKA): Demonstrates proficiency in managing Kubernetes clusters, a critical skill in MLOps.
• AWS Certified Machine Learning – Specialty: Validates expertise in building, training, and deploying machine learning models on AWS.
• Google Professional Machine Learning Engineer: Focuses on designing, building, and productionizing ML models on Google Cloud.
• Microsoft Certified: Azure AI Engineer Associate: Covers AI and ML solutions on the Azure platform.

Experience Required for Senior MLOps Engineer Roles

Typically, a Senior MLOps Engineer role requires 5-10 years of experience in software engineering, data science, or a related field. Experience in deploying machine learning models, managing data pipelines, and working with cloud platforms is crucial. Additionally, experience in DevOps practices, containerization, and orchestration tools like Docker and Kubernetes is often required. Leadership experience or a proven track record of managing projects can also be beneficial.

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