Salary for Mid-level / Intermediate Full Stack Engineer during 2024

Salary details

The average mid-level / intermediate Full Stack Engineer salary lies between USD 120,000 and USD 177,100 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: Dec. 22, 2024

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

The three most common job tag items assiciated with mid-level / intermediate Full Stack Engineer job listings are Python, React 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 | 51 jobs React | 51 jobs Machine Learning | 49 jobs Engineering | 49 jobs APIs | 38 jobs JavaScript | 34 jobs Docker | 27 jobs Computer Science | 25 jobs Agile | 24 jobs Testing | 21 jobs Node.js | 21 jobs Kubernetes | 20 jobs AWS | 20 jobs Security | 19 jobs DevOps | 19 jobs MongoDB | 17 jobs Flask | 17 jobs GitHub | 17 jobs CI/CD | 16 jobs Architecture | 15 jobs

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

The three most common job benefits and perks assiciated with mid-level / intermediate Full Stack Engineer job listings are Career development, Startup environment 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 | 43 jobs Startup environment | 28 jobs Flex hours | 27 jobs Health care | 23 jobs Competitive pay | 16 jobs Insurance | 11 jobs Equity / stock options | 10 jobs Fitness / gym | 10 jobs Team events | 8 jobs Parental leave | 6 jobs Flex vacation | 6 jobs Salary bonus | 6 jobs Medical leave | 5 jobs Unlimited paid time off | 5 jobs Relocation support | 4 jobs Home office stipend | 3 jobs 401(k) matching | 2 jobs Wellness | 2 jobs Pet friendly | 2 jobs Flat hierarchy | 1 jobs

Salary Composition

The salary for a mid-level AI/ML/Data Science role typically comprises 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 forms the bulk of the total compensation package. Performance bonuses can vary significantly depending on the company's success and individual performance, often ranging from 10% to 20% of the base salary. Additional remuneration like stock options is more common in startups and large tech firms, providing long-term incentives aligned with company growth. Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York are generally higher than in other regions. Industry-wise, tech companies tend to offer more competitive packages compared to sectors like finance or healthcare, although these industries are increasingly investing in AI/ML capabilities.

Increasing Salary Further

To increase your salary beyond the median of USD 150,000, consider specializing in high-demand areas within AI/ML, such as deep learning, natural language processing, or computer vision. Gaining expertise in these niches can make you more valuable to employers. Additionally, pursuing leadership roles or transitioning into a managerial position can significantly boost your earning potential. Networking within the industry and building a strong professional brand through speaking engagements, publications, or contributions to open-source projects can also open doors to higher-paying opportunities. Finally, consider relocating to regions or industries that offer higher compensation for AI/ML roles.

Educational Requirements

Most mid-level AI/ML/Data Science positions require at least a bachelor's degree in computer science, data science, mathematics, statistics, or a related field. However, a master's degree or Ph.D. can be advantageous and sometimes necessary, especially for roles that involve complex research or algorithm development. Employers often look for candidates with a strong foundation in programming, data structures, algorithms, and statistical analysis. Practical experience through internships, projects, or previous work in related fields is also highly valued.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile and demonstrate your commitment to the field. Certifications such as the Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, or the Microsoft Certified: Azure AI Engineer Associate are well-regarded in the industry. These certifications validate your skills in deploying machine learning models on cloud platforms, which is a critical aspect of many AI/ML roles. Additionally, completing courses from platforms like Coursera, edX, or Udacity in specialized areas of AI/ML can further bolster your credentials.

Required Experience

Typically, a mid-level position requires 3-5 years of experience in software development, data analysis, or a related field. Experience in full-stack development is beneficial, as it provides a comprehensive understanding of both front-end and back-end systems, which is crucial for implementing AI/ML solutions. Employers look for candidates who have hands-on experience with machine learning frameworks (such as TensorFlow or PyTorch), data manipulation tools (like Pandas or SQL), and cloud services (AWS, Azure, or Google Cloud).

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