Salary for Senior-level / Expert Platform Engineer during 2024

Salary details

The average senior-level / expert Platform Engineer salary lies between USD 136,000 and USD 249,840 globally. 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.

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 Senior-level / Expert Platform Engineer roles

The three most common job tag items assiciated with senior-level / expert Platform Engineer job listings are Python, Engineering and Machine Learning. 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 | 241 jobs Engineering | 240 jobs Machine Learning | 206 jobs AWS | 187 jobs Computer Science | 162 jobs Kubernetes | 153 jobs Architecture | 151 jobs Security | 139 jobs Pipelines | 138 jobs Terraform | 138 jobs Azure | 134 jobs DevOps | 112 jobs CI/CD | 111 jobs GCP | 110 jobs Docker | 95 jobs APIs | 90 jobs Spark | 83 jobs MLOps | 80 jobs Agile | 80 jobs Java | 76 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Platform Engineer job listings are Career development, Equity / stock options 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 | 218 jobs Equity / stock options | 115 jobs Flex hours | 113 jobs Health care | 98 jobs Team events | 78 jobs Medical leave | 72 jobs Parental leave | 68 jobs Flex vacation | 60 jobs Startup environment | 60 jobs Salary bonus | 59 jobs Competitive pay | 57 jobs Insurance | 48 jobs Wellness | 45 jobs Transparency | 30 jobs Home office stipend | 29 jobs 401(k) matching | 14 jobs Gear | 13 jobs Relocation support | 11 jobs Fitness / gym | 9 jobs Flexible spending account | 9 jobs

Salary Composition

The salary for a Senior-level/Expert Platform Engineer in AI/ML/Data Science typically comprises a base salary, bonuses, and additional remuneration such as stock options or equity. The base salary is often the largest component, accounting for 70-80% of the total compensation package. Bonuses, which can be performance-based or company-wide, usually make up 10-20%. Additional remuneration, such as stock options, equity, or profit-sharing, can vary significantly depending on the company size and industry. In tech hubs like Silicon Valley, equity can be a substantial part of the package, especially in startups. In contrast, larger, more established companies might offer more in terms of bonuses and benefits.

Increasing Salary

To increase your salary from this position, consider pursuing leadership roles such as Engineering Manager or Director of Engineering. These roles often come with higher compensation and more responsibility. Additionally, specializing in a niche area of AI/ML, such as natural language processing or computer vision, can make you more valuable. Networking within industry circles and attending conferences can also open up opportunities for higher-paying roles. Finally, consider negotiating your salary during performance reviews or when taking on additional responsibilities.

Educational Requirements

Most senior-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 or Ph.D. can be advantageous, especially for roles that require deep technical expertise or research capabilities. Advanced degrees can also provide a competitive edge in the job market and may lead to higher starting salaries.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile. Certifications in cloud platforms like AWS Certified Solutions Architect, Google Cloud Professional Data Engineer, or Microsoft Certified: Azure Solutions Architect Expert are highly regarded. Additionally, certifications in data science and machine learning, such as the TensorFlow Developer Certificate or the Certified Data Scientist (CDS) credential, can demonstrate your expertise and commitment to the field.

Required Experience

Typically, a senior-level platform engineer in AI/ML/Data Science is expected to have 7-10 years of experience in software engineering, with a significant portion of that time spent working on AI/ML projects. Experience in designing and deploying scalable systems, working with large datasets, and collaborating with cross-functional teams is crucial. Leadership experience, such as managing teams or projects, is often required for senior roles.

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