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

Salary details

The average mid-level / intermediate Platform Engineer salary lies between USD 109,000 and USD 187,000 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. 24, 2024

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

The three most common job tag items assiciated with mid-level / intermediate Platform Engineer job listings are Python, Engineering and AWS. 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:

Python | 103 jobs Engineering | 98 jobs AWS | 72 jobs Machine Learning | 65 jobs Security | 61 jobs Kubernetes | 58 jobs Agile | 53 jobs Architecture | 51 jobs Azure | 50 jobs Pipelines | 50 jobs CI/CD | 48 jobs DevOps | 47 jobs Computer Science | 46 jobs Java | 40 jobs Terraform | 39 jobs SQL | 34 jobs GCP | 34 jobs Linux | 30 jobs Docker | 30 jobs Spark | 28 jobs

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

The three most common job benefits and perks assiciated with mid-level / intermediate Platform 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 | 96 jobs Health care | 47 jobs Equity / stock options | 32 jobs Flex hours | 32 jobs Startup environment | 32 jobs Competitive pay | 31 jobs Salary bonus | 30 jobs Team events | 24 jobs Flex vacation | 19 jobs Insurance | 19 jobs Medical leave | 17 jobs Wellness | 15 jobs Parental leave | 14 jobs Fitness / gym | 7 jobs Home office stipend | 6 jobs Transparency | 5 jobs Conferences | 5 jobs Fertility benefits | 5 jobs 401(k) matching | 4 jobs Travel | 4 jobs

Salary Composition

In the United States, the salary composition for a Mid-level/Intermediate Platform Engineer in AI/ML/Data Science 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. Performance bonuses can vary significantly depending on the company's profitability and individual performance metrics. Additional remuneration, like stock options, is more common in larger tech companies or startups, where equity is used as an incentive to attract and retain talent. The composition can also vary by region, with tech hubs like San Francisco or New York offering higher base salaries but potentially lower relative bonuses due to the high cost of living. In contrast, companies in smaller cities might offer a more balanced package with a higher emphasis on bonuses.

Increasing Salary

To increase your salary from this position, consider pursuing advanced roles such as Senior Platform Engineer, Technical Lead, or Engineering Manager. These roles often require a combination of technical expertise, leadership skills, and strategic thinking. Additionally, specializing in high-demand areas within AI/ML, such as deep learning, natural language processing, or AI ethics, can make you more valuable. Networking within the industry, attending conferences, and contributing to open-source projects can also enhance your visibility and open up opportunities for higher-paying positions. Further education, such as a master's degree or specialized certifications, can also be a pathway to salary growth.

Educational Requirements

Most mid-level platform engineering roles in AI/ML/Data Science require at least a bachelor's degree in computer science, engineering, mathematics, 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 analysis techniques. A strong foundation in programming, data structures, and algorithms is essential, along with coursework in statistics and machine learning.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise to potential employers. Certifications such as the Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, and Microsoft Certified: Azure AI Engineer Associate are well-regarded in the industry. These certifications validate your skills in deploying machine learning models and managing AI solutions on cloud platforms, which are critical skills for platform engineers.

Required Experience

Typically, a mid-level platform engineer role requires 3-5 years of experience in software engineering or a related field. Experience with cloud platforms (such as AWS, Azure, or Google Cloud), containerization technologies (like Docker and Kubernetes), and CI/CD pipelines is often expected. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch, or Scikit-learn) and data processing tools (like Apache Spark or Hadoop) is also beneficial. Experience in collaborating with data scientists and understanding their workflows can be a significant advantage.

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