Solutions Engineer Salary in United States during 2024

Salary details

The average Solutions Engineer salary lies between USD 111,000 and USD 204,700 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 Solutions Engineer roles

The three most common job tag items assiciated with 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 | 350 jobs Engineering | 339 jobs Machine Learning | 264 jobs Architecture | 186 jobs Computer Science | 180 jobs Java | 168 jobs Big Data | 130 jobs AWS | 130 jobs SQL | 129 jobs Azure | 117 jobs GCP | 116 jobs Spark | 108 jobs Generative AI | 104 jobs Security | 102 jobs Data Analytics | 101 jobs Databricks | 100 jobs Research | 97 jobs JavaScript | 95 jobs APIs | 95 jobs Excel | 92 jobs

Top 20 Job Perks/Benefits for Solutions Engineer roles

The three most common job benefits and perks assiciated with 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 | 298 jobs Health care | 185 jobs Team events | 123 jobs Equity / stock options | 120 jobs Insurance | 98 jobs Startup environment | 95 jobs Salary bonus | 95 jobs Flex hours | 94 jobs Competitive pay | 93 jobs Wellness | 89 jobs Parental leave | 83 jobs Medical leave | 81 jobs Flex vacation | 72 jobs Fitness / gym | 49 jobs 401(k) matching | 46 jobs Conferences | 32 jobs Transparency | 26 jobs Home office stipend | 22 jobs Gear | 21 jobs Unlimited paid time off | 20 jobs

Salary Composition for AI/ML/Data Science Solutions Engineer

The salary for a Solutions Engineer in AI/ML/Data Science typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or benefits. 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 total salary, depending on individual and company performance. Additional remuneration, such as stock options, profit sharing, or other benefits, can make up the remaining 5-10%.

Regional differences can influence these percentages, with tech hubs like Silicon Valley or New York City offering higher base salaries and more lucrative stock options due to the higher cost of living and competitive job market. Industry also plays a role; for instance, tech companies may offer more in stock options, while finance or healthcare sectors might provide higher bonuses. Company size can affect the composition as well, with larger companies often providing more comprehensive benefits packages.

Steps to Increase Salary from This Position

To increase your salary from a Solutions Engineer position, consider the following strategies:

• Skill Enhancement: Continuously update your technical skills, especially in emerging AI/ML technologies, to stay ahead in the field.
• Advanced Education: Pursuing a master's degree or specialized certifications can make you more valuable to employers.
• Networking: Build a strong professional network to learn about higher-paying opportunities and gain insights into industry trends.
• Leadership Roles: Seek opportunities to lead projects or teams, which can position you for promotions to managerial roles.
• Industry Transition: Consider moving to industries with higher pay scales for AI/ML professionals, such as finance or biotech.

Educational Requirements

Most Solutions Engineer 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 demand a deep understanding of machine learning algorithms and data analysis techniques. Some positions may also require coursework in business or communication to effectively translate technical solutions to non-technical stakeholders.

Helpful Certifications

Certifications can bolster your credentials and demonstrate expertise in specific areas. Some valuable certifications include:

• Certified Solutions Architect (AWS): Demonstrates proficiency in designing and deploying scalable systems on AWS.
• Google Professional Machine Learning Engineer: Validates your ability to design, build, and productionize ML models on Google Cloud.
• Microsoft Certified: Azure AI Engineer Associate: Focuses on using Azure AI services to build and integrate AI solutions.
• Data Science Certifications: Such as those offered by Coursera or edX, which cover a range of data science and machine learning topics.

Experience Requirements

Typically, a Solutions Engineer in AI/ML/Data Science is expected to have 3-5 years of experience in a related field. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience in customer-facing roles or project management can also be beneficial, as these positions often require translating technical solutions into business value for clients.

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