Salary for Mid-level / Intermediate Systems Engineer in United States during 2024

Salary details

The average mid-level / intermediate Systems Engineer salary lies between USD 104,550 and USD 183,000 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 Mid-level / Intermediate Systems Engineer roles

The three most common job tag items assiciated with mid-level / intermediate Systems 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 | 91 jobs Python | 82 jobs Machine Learning | 56 jobs Architecture | 56 jobs Security | 51 jobs Computer Science | 49 jobs Testing | 44 jobs Research | 32 jobs Linux | 30 jobs Pipelines | 27 jobs Physics | 24 jobs Matlab | 22 jobs Kubernetes | 22 jobs AWS | 20 jobs SQL | 19 jobs Agile | 19 jobs Privacy | 18 jobs Mathematics | 17 jobs Java | 16 jobs Data Analytics | 15 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate Systems Engineer roles

The three most common job benefits and perks assiciated with mid-level / intermediate Systems Engineer job listings are Career development, Health care and Insurance. 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 | 69 jobs Health care | 56 jobs Insurance | 35 jobs Flex hours | 33 jobs Equity / stock options | 32 jobs Flex vacation | 26 jobs Competitive pay | 25 jobs Parental leave | 23 jobs Salary bonus | 21 jobs Medical leave | 20 jobs Team events | 15 jobs 401(k) matching | 13 jobs Startup environment | 11 jobs Relocation support | 11 jobs Wellness | 10 jobs Signing bonus | 8 jobs Conferences | 5 jobs Flexible spending account | 5 jobs Fertility benefits | 5 jobs Transparency | 4 jobs

Salary Composition

In the United States, the salary composition for a Mid-level/Intermediate Systems Engineer in AI/ML/Data Science typically includes a base salary, performance bonuses, and additional remuneration such as stock options or profit-sharing. The base salary is often the largest component, accounting for approximately 70-80% of the total compensation package. Performance bonuses can vary significantly depending on the company and industry, ranging from 10-20% of the base salary. Additional remuneration, such as stock options, is more common in tech companies and startups, potentially adding another 5-10% to the total compensation. 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. Similarly, larger companies may offer more comprehensive compensation packages compared to smaller firms.

Increasing Salary

To increase your salary from this position, consider pursuing advanced roles such as Senior Systems Engineer, Data Scientist, or Machine Learning Engineer. Gaining expertise in specialized areas like deep learning, natural language processing, or big data analytics can make you more valuable. Additionally, developing leadership skills and taking on project management responsibilities can position you for roles with higher pay. Networking within the industry and seeking opportunities in high-demand sectors such as finance, healthcare, or autonomous systems can also lead to higher salaries. Continuous learning and staying updated with the latest technologies and trends in AI/ML will further enhance your career prospects.

Educational Requirements

Most positions 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. is often preferred, especially for roles involving complex algorithm development or research. Advanced degrees provide a deeper understanding of machine learning models, statistical analysis, and data manipulation techniques, which are crucial for success in this field. Additionally, coursework in artificial intelligence, data mining, and software engineering can be highly beneficial.

Helpful Certifications

Certifications can bolster 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 validate your skills in specific tools and platforms commonly used in AI/ML projects and can give you a competitive edge in the job market.

Required Experience

Typically, a mid-level position in AI/ML/Data Science requires 3-5 years of relevant experience. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience with programming languages such as Python, R, or Java, and familiarity with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn, is essential. Additionally, experience in data visualization and working with large datasets is often required.

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