Salary for Mid-level / Intermediate MLOps Engineer in United States during 2024

Salary details

The average mid-level / intermediate MLOps Engineer salary lies between USD 114,800 and USD 175,000 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

Top 20 Job Tags for Mid-level / Intermediate MLOps Engineer roles

The three most common job tag items assiciated with mid-level / intermediate MLOps Engineer job listings are MLOps, Machine Learning and ML models. 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 | 49 jobs Machine Learning | 48 jobs ML models | 41 jobs Python | 40 jobs Engineering | 40 jobs Pipelines | 34 jobs Computer Science | 30 jobs AWS | 29 jobs Azure | 27 jobs Kubernetes | 26 jobs Docker | 26 jobs GCP | 25 jobs DevOps | 25 jobs CI/CD | 25 jobs Testing | 22 jobs PyTorch | 19 jobs Model training | 18 jobs TensorFlow | 16 jobs MLFlow | 16 jobs Research | 15 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate MLOps Engineer roles

The three most common job benefits and perks assiciated with mid-level / intermediate MLOps Engineer job listings are Career development, Health care and Competitive pay. 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 | 32 jobs Health care | 14 jobs Competitive pay | 14 jobs Flex hours | 12 jobs Startup environment | 12 jobs Salary bonus | 7 jobs Equity / stock options | 6 jobs Team events | 6 jobs Gear | 4 jobs Parental leave | 3 jobs Wellness | 2 jobs Insurance | 2 jobs 401(k) matching | 1 jobs Lunch / meals | 1 jobs Flex vacation | 1 jobs Transparency | 1 jobs Yoga | 1 jobs Medical leave | 1 jobs Home office stipend | 1 jobs Unlimited paid time off | 1 jobs

Salary Composition for a Mid-level MLOps Engineer

The salary for a Mid-level 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, making up about 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 or startups and can add significant value to the overall compensation package. The composition can vary by region, with tech hubs like San Francisco or New York offering higher base salaries and more substantial equity components 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.

Steps to Increase Salary from a Mid-level Position

To increase your salary beyond the mid-level MLOps Engineer position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging technologies and tools in the AI/ML space. Specializing in niche areas like AI ethics, advanced data engineering, or cloud-native MLOps can make you more valuable.
• Leadership Roles: Aim for roles that involve team leadership or project management. Demonstrating leadership skills can position you for senior roles with higher pay.
• Networking: Build a strong professional network. Engaging with industry peers can open up opportunities for higher-paying positions.
• Certifications: Obtain advanced certifications that are recognized in the industry, which can justify a higher salary.
• Industry Switch: Consider switching to industries that pay more for MLOps expertise, such as finance or healthcare.

Educational Requirements

Most mid-level MLOps Engineer positions require at least a bachelor's degree in computer science, data science, engineering, or a related field. However, a master's degree can be advantageous and is often preferred by employers, especially for roles that require a deeper understanding of machine learning algorithms and data infrastructure. Some positions might also value a background in mathematics or statistics, given the analytical nature of the work.

Helpful Certifications

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

• Certified Kubernetes Administrator (CKA): As Kubernetes is a critical tool in MLOps, this certification is highly regarded.
• AWS Certified Machine Learning – Specialty: Demonstrates expertise in deploying machine learning models on AWS.
• Google Professional Machine Learning Engineer: Validates your ability to design, build, and productionize ML models on Google Cloud.
• TensorFlow Developer Certificate: Shows proficiency in using TensorFlow for machine learning tasks.

Required Experience

Typically, a mid-level MLOps Engineer is expected to have 3-5 years of experience in related roles. This experience should include hands-on work with machine learning models, data pipelines, and cloud platforms. Experience with DevOps practices, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes) is also crucial. Familiarity with CI/CD pipelines and version control systems is often required.

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