Solutions Engineer Salary in United States during 2024

Salary details

The average Solutions Engineer salary lies between USD 113,700 and USD 205,100 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 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 | 281 jobs Engineering | 279 jobs Machine Learning | 212 jobs Architecture | 156 jobs Computer Science | 151 jobs Java | 130 jobs AWS | 112 jobs Big Data | 102 jobs SQL | 101 jobs Azure | 100 jobs GCP | 94 jobs Generative AI | 91 jobs Spark | 90 jobs APIs | 86 jobs Research | 83 jobs Databricks | 83 jobs Data Analytics | 79 jobs Excel | 74 jobs Security | 72 jobs MLFlow | 72 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 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 | 249 jobs Health care | 167 jobs Equity / stock options | 108 jobs Team events | 97 jobs Insurance | 88 jobs Salary bonus | 88 jobs Wellness | 84 jobs Competitive pay | 80 jobs Startup environment | 76 jobs Flex hours | 75 jobs Parental leave | 70 jobs Medical leave | 70 jobs Flex vacation | 64 jobs Fitness / gym | 47 jobs 401(k) matching | 42 jobs Transparency | 23 jobs Conferences | 23 jobs Home office stipend | 19 jobs Gear | 18 jobs Unlimited paid time off | 16 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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