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

Salary details

The average senior-level / expert Engineer salary lies between USD 136,800 and USD 230,000 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. 10, 2024

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

The three most common job tag items assiciated with senior-level / expert Engineer job listings are Engineering, Python 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:

Engineering | 23606 jobs Python | 22826 jobs Machine Learning | 17178 jobs Computer Science | 14535 jobs Architecture | 13424 jobs Pipelines | 12788 jobs AWS | 12723 jobs SQL | 12566 jobs Security | 9922 jobs Testing | 9435 jobs Java | 8469 jobs Agile | 8288 jobs Azure | 8283 jobs Spark | 8062 jobs Data pipelines | 7528 jobs Research | 7083 jobs GCP | 6966 jobs ETL | 6833 jobs Big Data | 6694 jobs Kubernetes | 6360 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert 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 | 21546 jobs Health care | 12224 jobs Equity / stock options | 9723 jobs Flex hours | 7983 jobs Competitive pay | 7230 jobs Startup environment | 6938 jobs Salary bonus | 5972 jobs Flex vacation | 5963 jobs Team events | 5815 jobs Insurance | 5304 jobs Medical leave | 5254 jobs Parental leave | 5064 jobs Wellness | 3403 jobs 401(k) matching | 3210 jobs Conferences | 1517 jobs Home office stipend | 1478 jobs Relocation support | 1427 jobs Flexible spending account | 1173 jobs Transparency | 1168 jobs Unlimited paid time off | 1071 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Engineer in AI/ML/Data Science typically includes a combination of a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the largest portion, ranging from 60% to 80% of the total compensation package. Bonuses can vary significantly depending on the company's performance and individual contributions, usually accounting for 10% to 20% of the total salary. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and may range from 10% to 30% of the total compensation.

Regional differences also play a role; for instance, salaries in tech hubs like San Francisco, New York, and Seattle tend to be higher due to the cost of living and demand for talent. Industry-wise, tech companies, finance, and healthcare often offer higher compensation compared to academia or government roles. Company size can also influence salary composition, with larger companies typically offering more comprehensive benefits and stock options.

Increasing Salary Further

To increase your salary beyond the median of USD 178,050, consider the following strategies:

• Specialization: Develop expertise in niche areas of AI/ML, such as deep learning, natural language processing, or computer vision, which are in high demand.
• Leadership Roles: Transition into leadership or managerial positions, such as AI/ML team lead or director, which often come with higher pay.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML through courses, workshops, and conferences.
• Networking: Build a strong professional network to learn about new opportunities and gain insights into industry trends.
• Consulting or Freelancing: Consider consulting or freelancing on the side, which can provide additional income and broaden your experience.

Educational Requirements

For a Senior-level or Expert Engineer position in AI/ML/Data Science, a strong educational background is typically required. Most candidates hold at least a Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. A Ph.D. is often preferred, especially for roles involving research and development of new algorithms or technologies. Advanced degrees demonstrate a deep understanding of complex concepts and the ability to conduct independent research.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate expertise in specific areas. Some valuable certifications include:

• Certified Machine Learning Professional (CMLP)
• TensorFlow Developer Certificate
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Data Science Council of America (DASCA) Senior Data Scientist (SDS)

These certifications can validate your skills and knowledge, making you a more attractive candidate to potential employers.

Required Experience

Typically, a Senior-level or Expert Engineer in AI/ML/Data Science is expected to have at least 5 to 10 years of relevant experience. This includes hands-on experience with machine learning algorithms, data analysis, and software development. Experience in leading projects, mentoring junior engineers, and collaborating with cross-functional teams is also highly valued. Demonstrated success in deploying AI/ML solutions in real-world applications is crucial.

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