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

Salary details

The average senior-level / expert Full Stack Engineer salary lies between USD 148,000 and USD 224,000 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: Dec. 22, 2024

Top 20 Job Tags for Senior-level / Expert Full Stack Engineer roles

The three most common job tag items assiciated with senior-level / expert 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 | 109 jobs React | 105 jobs Machine Learning | 100 jobs Engineering | 98 jobs APIs | 87 jobs AWS | 74 jobs JavaScript | 64 jobs Architecture | 62 jobs Testing | 61 jobs Docker | 61 jobs Computer Science | 58 jobs Agile | 51 jobs Kubernetes | 50 jobs Java | 50 jobs Node.js | 49 jobs Angular | 43 jobs TypeScript | 40 jobs Security | 39 jobs Pipelines | 39 jobs CI/CD | 39 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Full Stack Engineer job listings are Career development, Flex hours and Startup environment. 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 | 88 jobs Flex hours | 53 jobs Startup environment | 53 jobs Health care | 49 jobs Equity / stock options | 47 jobs Flex vacation | 37 jobs Competitive pay | 30 jobs Medical leave | 26 jobs Parental leave | 25 jobs Team events | 25 jobs Insurance | 24 jobs Home office stipend | 19 jobs 401(k) matching | 18 jobs Salary bonus | 17 jobs Fitness / gym | 15 jobs Wellness | 13 jobs Unlimited paid time off | 13 jobs Gear | 10 jobs Relocation support | 10 jobs Travel | 8 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Full Stack Engineer in AI/ML/Data Science typically includes a combination of a fixed 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, accounting for approximately 70-80% of the total compensation package. Performance bonuses can vary significantly, ranging from 10-20% of the base salary, depending on individual and company performance. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and can vary widely based on the company's valuation and growth prospects.

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 market. Industry can also influence salary composition, with finance and healthcare sectors often offering higher bonuses compared to other industries. Company size is another factor; larger companies may offer more comprehensive benefits and stock options, while smaller companies might provide higher base salaries to attract talent.

Increasing Salary

To increase your salary further from this position, consider the following strategies:

• Specialization: Develop expertise in a niche area within AI/ML, such as natural language processing, computer vision, or reinforcement learning. Specialized skills are often in high demand and can command higher salaries.
• Leadership Roles: Transition into leadership or managerial roles, such as a team lead or engineering manager, which typically offer higher compensation.
• Continuous Learning: Stay updated with the latest technologies and trends in AI/ML. Pursuing advanced certifications or courses can enhance your skill set and make you more valuable to employers.
• Networking: Build a strong professional network within the industry. Networking can lead to opportunities for higher-paying positions or consulting roles.
• Negotiation Skills: Improve your negotiation skills to better advocate for higher compensation during job offers or performance reviews.

Educational Requirements

For a Senior-level or Expert Full Stack Engineer in AI/ML/Data Science, a bachelor's degree in computer science, engineering, mathematics, or a related field is typically required. However, many employers prefer candidates with a master's degree or even a Ph.D. in a relevant discipline, especially for roles that involve complex problem-solving and research. Advanced degrees can provide a deeper understanding of machine learning algorithms, data analysis, and software engineering principles, which are crucial for this role.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise to potential employers. Some valuable certifications include:

• Certified Machine Learning Professional (CMLP)
• TensorFlow Developer Certificate
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Google Professional Machine Learning Engineer

These certifications can validate your skills in specific tools and platforms commonly used in AI/ML projects.

Required Experience

Typically, a Senior-level or Expert Full Stack Engineer in AI/ML/Data Science is expected to have at least 5-10 years of experience in software development, with a significant portion of that time spent working on AI/ML projects. Experience in full stack development, including both front-end and back-end technologies, is crucial. Additionally, hands-on experience with machine learning frameworks, data analysis, and cloud platforms is often required. Demonstrated experience in leading projects or teams can also be a significant advantage.

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