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

Salary details

The average senior-level / expert Solutions Engineer salary lies between USD 135,400 and USD 220,000 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 Solutions Engineer roles

The three most common job tag items assiciated with senior-level / expert Solutions 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 | 203 jobs Engineering | 188 jobs Machine Learning | 137 jobs Architecture | 125 jobs Java | 100 jobs Spark | 96 jobs AWS | 94 jobs Big Data | 93 jobs Databricks | 88 jobs Excel | 87 jobs Azure | 85 jobs MLFlow | 85 jobs Computer Science | 82 jobs Scala | 77 jobs GCP | 72 jobs SQL | 71 jobs Data Analytics | 66 jobs Generative AI | 65 jobs Security | 58 jobs APIs | 53 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Solutions Engineer job listings are Career development, Health care and Team events. 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 | 156 jobs Health care | 102 jobs Team events | 70 jobs Insurance | 69 jobs Salary bonus | 59 jobs Wellness | 57 jobs Equity / stock options | 56 jobs Flex hours | 52 jobs Competitive pay | 48 jobs Parental leave | 42 jobs Startup environment | 42 jobs Medical leave | 42 jobs Flex vacation | 41 jobs Fitness / gym | 40 jobs Conferences | 19 jobs Transparency | 18 jobs 401(k) matching | 17 jobs Home office stipend | 16 jobs Gear | 15 jobs Signing bonus | 13 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Solutions Engineer in AI/ML/Data Science typically includes a combination of base salary, bonuses, and additional remuneration such as stock options or equity. The base salary often constitutes the largest portion, ranging from 60% to 80% of the total compensation package. Bonuses, which can be performance-based or tied to company success, usually account for 10% to 20%. Additional remuneration, such as stock options, equity, or other benefits, can make up the remaining 10% to 20%.

The composition can vary significantly depending on the region, industry, and company size. For instance, tech hubs like Silicon Valley or New York City might offer higher base salaries and more substantial equity packages. In contrast, companies in smaller markets might offer lower base salaries but compensate with higher bonuses or other benefits. Similarly, larger tech companies or those in highly competitive industries may provide more lucrative stock options compared to smaller firms.

Increasing Salary Further

To increase your salary beyond the median of USD 192,000, consider the following strategies:

• Specialization: Develop expertise in niche areas of AI/ML that are in high demand but have a limited talent pool, such as natural language processing, computer vision, or reinforcement learning.
• Leadership Roles: Transition into leadership or managerial roles, such as AI/ML team lead or director of data science, which typically offer higher compensation.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML. Pursuing advanced certifications or a master's degree in a specialized field can also enhance your qualifications.
• Networking: Build a strong professional network to learn about higher-paying opportunities and gain insights into industry trends.
• Negotiation Skills: Improve your negotiation skills to better advocate for higher compensation during job offers or performance reviews.

Educational Requirements

Most senior-level positions in AI/ML/Data Science require at least a bachelor's degree in a related field such as computer science, data science, mathematics, or engineering. However, a master's degree or Ph.D. is often preferred, especially for roles that involve complex problem-solving and research. Advanced degrees can provide a deeper understanding of machine learning algorithms, statistical methods, and data analysis techniques, which are crucial for success in this field.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credibility 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, making you a more attractive candidate for senior-level positions.

Required Experience

Typically, a Senior-level or Expert Solutions Engineer in AI/ML/Data Science is expected to have at least 5 to 10 years of relevant experience. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience in leading projects, mentoring junior engineers, and collaborating with cross-functional teams is also highly valued. Additionally, a proven track record of successful AI/ML implementations and contributions to significant projects can set you apart from other candidates.

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