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

Salary details

The average senior-level / expert Systems Engineer salary lies between USD 130,000 and USD 213,000 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. 24, 2024

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

The three most common job tag items assiciated with senior-level / expert 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 | 237 jobs Python | 207 jobs Machine Learning | 142 jobs Architecture | 139 jobs Testing | 138 jobs Computer Science | 129 jobs Security | 125 jobs Research | 78 jobs AWS | 64 jobs Linux | 63 jobs Mathematics | 63 jobs Kubernetes | 60 jobs PhD | 60 jobs Matlab | 54 jobs Agile | 53 jobs Java | 53 jobs Azure | 48 jobs Docker | 48 jobs Physics | 48 jobs Pipelines | 46 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Systems 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 | 176 jobs Health care | 131 jobs Equity / stock options | 107 jobs Insurance | 71 jobs Startup environment | 68 jobs Flex vacation | 66 jobs Competitive pay | 65 jobs Medical leave | 65 jobs Salary bonus | 61 jobs Flex hours | 60 jobs Parental leave | 56 jobs Team events | 48 jobs 401(k) matching | 36 jobs Wellness | 35 jobs Relocation support | 32 jobs Fertility benefits | 25 jobs Unlimited paid time off | 18 jobs Flexible spending account | 16 jobs Lunch / meals | 12 jobs Signing bonus | 11 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Systems Engineer in AI/ML/Data Science typically includes a combination of a fixed base salary, performance bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the majority of the total compensation package, ranging from 70% to 85%. Performance bonuses can vary significantly, often comprising 10% to 20% of the total compensation, depending on individual and company performance. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and may account for 5% to 15% of the total compensation. The exact composition can vary based on the region, with tech hubs like Silicon Valley offering higher equity components, and industry, with finance and healthcare sectors sometimes offering higher bonuses. Company size also plays a role, with larger companies often providing more structured bonus and equity packages.

Increasing Salary Further

To increase your salary beyond the median of USD 165,900, consider pursuing leadership roles such as a Director of Engineering or a Chief Data Scientist. These positions often come with higher compensation packages. Additionally, specializing in high-demand areas like deep learning, natural language processing, or AI ethics can make you more valuable. Networking within industry circles and attending conferences can also open up opportunities for higher-paying roles. Another strategy is to negotiate your salary by leveraging offers from other companies, which can sometimes lead to a counteroffer from your current employer. Continuous learning and obtaining advanced certifications can also justify a salary increase.

Educational Requirements

Most senior-level 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 that involve complex problem-solving and research. Advanced degrees provide a deeper understanding of algorithms, data structures, and statistical methods, which are crucial for developing sophisticated AI models. Additionally, coursework in machine learning, data mining, and big data technologies is highly beneficial.

Helpful Certifications

While not always mandatory, certain certifications can enhance 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
• Data Science Council of America (DASCA) Senior Data Scientist (SDS) Certification

These certifications can validate your skills in specific tools and platforms, making you more competitive in the job market.

Required Experience

Typically, a senior-level position in this field requires at least 5 to 10 years of experience in systems engineering, with a significant portion of that time spent working on AI/ML projects. Experience in designing and deploying machine learning models, managing data pipelines, and working with large datasets is crucial. Additionally, experience in leading teams, managing projects, and collaborating with cross-functional teams is often required. Familiarity with programming languages such as Python, R, and Java, as well as experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn, is essential.

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