Salary for Executive-level / Director Engineer in United States during 2024

Salary details

The average executive-level / director Engineer salary lies between USD 170,000 and USD 290,000 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. 10, 2024

Top 20 Job Tags for Executive-level / Director Engineer roles

The three most common job tag items assiciated with executive-level / director Engineer job listings are Engineering, Python and Computer Science. 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 | 872 jobs Python | 594 jobs Computer Science | 508 jobs Machine Learning | 489 jobs Architecture | 484 jobs Security | 375 jobs AWS | 373 jobs Agile | 364 jobs SQL | 356 jobs Pipelines | 341 jobs Java | 308 jobs Testing | 294 jobs Big Data | 258 jobs ETL | 238 jobs Azure | 231 jobs Spark | 230 jobs Data pipelines | 220 jobs Research | 188 jobs Finance | 185 jobs Privacy | 180 jobs

Top 20 Job Perks/Benefits for Executive-level / Director Engineer roles

The three most common job benefits and perks assiciated with executive-level / director Engineer job listings are Career development, Health care and Startup environment. 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 | 759 jobs Health care | 382 jobs Startup environment | 297 jobs Equity / stock options | 243 jobs Flex hours | 224 jobs Competitive pay | 199 jobs Wellness | 198 jobs Salary bonus | 190 jobs Flex vacation | 179 jobs Insurance | 176 jobs Parental leave | 156 jobs Team events | 149 jobs Medical leave | 129 jobs 401(k) matching | 88 jobs Transparency | 33 jobs Conferences | 31 jobs Unlimited paid time off | 31 jobs Home office stipend | 27 jobs Relocation support | 20 jobs Fertility benefits | 17 jobs

Salary Composition

In the United States, the salary for an executive-level or director engineer position in AI/ML/Data Science typically comprises a base salary, performance 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. Performance bonuses can vary significantly, often between 10% to 30%, depending on the company's performance and individual achievements. Additional remuneration, such as stock options or equity, is more common in tech companies and startups, potentially making up 10% to 20% of the total package.

Regional differences can also impact salary composition. For instance, positions in tech hubs like Silicon Valley or New York City might offer higher base salaries and more substantial equity components due to the competitive market. Industry and company size also play a role; larger tech firms or those in high-demand sectors may offer more lucrative packages compared to smaller companies or those in traditional industries.

Increasing Salary Further

To increase your salary beyond the median of $250,000, consider pursuing roles with greater responsibility, such as VP of Engineering or Chief Data Officer. Expanding your skill set to include emerging technologies or business acumen can also make you more valuable. Networking within the industry and building a strong personal brand can open doors to higher-paying opportunities. Additionally, negotiating your compensation package effectively, including seeking higher equity stakes or performance bonuses, can lead to increased earnings.

Educational Requirements

For an executive-level position in AI/ML/Data Science, a strong educational background is typically required. Most candidates hold at least a master's degree in computer science, data science, machine learning, or a related field. A Ph.D. can be advantageous, especially for roles that require deep technical expertise or involve cutting-edge research. Business-related education, such as an MBA, can also be beneficial for roles that require strategic decision-making and leadership skills.

Helpful Certifications

While not always mandatory, certain certifications can enhance your qualifications and demonstrate expertise. Certifications such as the Certified Analytics Professional (CAP), TensorFlow Developer Certificate, or AWS Certified Machine Learning can be valuable. These certifications show a commitment to staying current with industry trends and technologies, which can be appealing to employers.

Required Experience

Typically, candidates for executive-level roles in AI/ML/Data Science have extensive experience, often 10-15 years or more, in relevant fields. This experience should include a mix of technical expertise, project management, and leadership roles. Experience in managing teams, developing and deploying AI/ML solutions, and driving strategic initiatives is crucial. A proven track record of successful projects and the ability to translate complex technical concepts into business value are also important.

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