Salary for Senior-level / Expert MLOps Engineer in United States during 2024

Salary details

The average senior-level / expert MLOps Engineer salary lies between USD 135,200 and USD 220,000 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 Senior-level / Expert MLOps Engineer roles

The three most common job tag items assiciated with senior-level / expert MLOps Engineer job listings are MLOps, Machine Learning 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:

MLOps | 220 jobs Machine Learning | 216 jobs Python | 188 jobs Pipelines | 183 jobs Engineering | 178 jobs Kubernetes | 160 jobs ML models | 152 jobs CI/CD | 152 jobs Docker | 136 jobs AWS | 132 jobs DevOps | 120 jobs PyTorch | 109 jobs TensorFlow | 104 jobs Testing | 102 jobs Azure | 97 jobs Computer Science | 90 jobs GCP | 88 jobs Architecture | 86 jobs MLFlow | 83 jobs Kubeflow | 80 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, Health care and Flex hours. 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 | 164 jobs Health care | 72 jobs Flex hours | 64 jobs Equity / stock options | 54 jobs Startup environment | 53 jobs Competitive pay | 52 jobs Team events | 49 jobs Flex vacation | 43 jobs Parental leave | 31 jobs Insurance | 30 jobs Medical leave | 25 jobs Salary bonus | 20 jobs Wellness | 17 jobs Transparency | 14 jobs 401(k) matching | 13 jobs Gear | 13 jobs Relocation support | 13 jobs Home office stipend | 13 jobs Conferences | 12 jobs Unlimited paid time off | 9 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 often forms the largest portion, ranging from 70% to 85% of the total compensation package. Performance bonuses can vary significantly, often constituting 10% to 20% of the total salary, depending on individual and company performance. Additional remuneration, such as stock options, is more common in larger tech companies and startups, potentially making up 5% to 15% of the total compensation. 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 compensation packages compared to other sectors.

Steps to Increase Salary from a Senior MLOps Engineer Position

To increase your salary beyond the Senior MLOps Engineer level, consider pursuing leadership roles such as MLOps Manager or Director of MLOps. These positions often come with higher compensation and greater responsibilities. Additionally, specializing in niche areas like AI ethics, data privacy, or scalable machine learning systems can make you more valuable. Networking within industry circles and attending conferences can also open up opportunities for higher-paying roles. Another strategy is to gain experience in high-demand industries such as finance or healthcare, where expertise in MLOps is particularly valued. Finally, consider negotiating your salary by leveraging offers from other companies or demonstrating your impact on business outcomes in your current role.

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. Additionally, coursework or experience in cloud computing, DevOps, and data engineering can be beneficial.

Helpful Certifications for MLOps Engineers

Certifications can enhance your credibility and demonstrate your expertise in specific areas. Some valuable certifications for MLOps Engineers include:

• AWS Certified Machine Learning – Specialty: Validates expertise in building, training, tuning, and deploying machine learning models on AWS.
• Google Professional Machine Learning Engineer: Demonstrates proficiency in designing, building, and productionizing ML models using Google Cloud technologies.
• Microsoft Certified: Azure AI Engineer Associate: Focuses on using Azure services to build, manage, and deploy AI solutions.
• Certified Kubernetes Administrator (CKA): Useful for managing containerized applications, which is a key aspect of MLOps.

Experience Required for Senior MLOps Engineer Roles

Typically, a Senior MLOps Engineer is expected to have 5 to 10 years of experience in related fields such as software engineering, data engineering, or machine learning. Experience with cloud platforms (AWS, Google Cloud, Azure), containerization (Docker, Kubernetes), and CI/CD pipelines is often required. Additionally, hands-on experience with machine learning frameworks (TensorFlow, PyTorch) and data processing tools (Apache Spark, Kafka) is highly valued. Leadership experience, such as leading projects or mentoring junior engineers, can also be beneficial.

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