There’s good earnings news for U.S. members: Salaries are rising. Base salaries increased by about 5 percent from 2022 to 2023, according to the IEEE-USA 2024 Salary and Benefits Survey Report.

Last year’s report showed that inflation had outpaced earnings growth but that’s not the case this year.

In current dollars, the median income of U.S. engineers and other tech professionals who are IEEE members was US $174,161 last year, up about 5 percent from $169,000 in 2022, excluding overtime pay, profit sharing, and other supplemental earnings. Unemployment fell to 1.2 percent in this year’s survey, down from 1.4 percent in the previous year.

As with prior surveys, earned income is measured for the year preceding the survey’s date of record—so the 2024 survey reports income earned in 2023.

To calculate the median salary, IEEE-USA considered only respondents who were tech professionals working full time in their primary area of competence—a sample of 4,192 people.

Circuits and device engineers earn the most

Those specializing in circuits and devices earned the highest median income, $196,614, followed by those working in communications ($190,000) and computers/software technology ($181,000).

Specific lucrative subspecialties include broadcast technology ($226,000), image/video ($219,015), and hardware design or hardware support ($215,000).

Engineers in the energy and power engineering field earned the lowest salary: $155,000.

Higher education affects how well one is paid. On average, those with a Ph.D. earned the highest median income: $193,636. Members with a master’s degree in electrical engineering or computer engineering reported a salary of $182,500. Those with a bachelor’s degree in electrical engineering or computer engineering earned a median income of $159,000.

Earning potential also depends on geography within the United States. Respondents in IEEE Region 6 (Western U.S.) fared substantially better than those in Region 4 (Central U.S.), earning nearly $48,500 more on average. However, the report notes, the cost of living in the western part of the country is significantly higher than elsewhere.

The top earners live in California, Maryland, and Oregon, while those earning the least live in Arkansas, Nebraska, and South Carolina.

Academics are among the lowest earners

Full professors earned an average salary of $190,000, associate professors earned $118,000, and assistant professors earned $104,500.

Almost 38 percent of the academics surveyed are full professors, 16.6 percent are associate professors, and 11.6 percent are assistant professors. About 10 percent of respondents hold a nonteaching research appointment. Nearly half (46.8 percent) are tenured, and 10.7 percent are on a tenure track.

Gender and ethnic gaps widen

The gap between women’s and men’s salaries increased. Even considering experience levels, women earned $30,515 less than their male counterparts.

The median primary income is highest among Asian/Pacific Islander technical professionals, at $178,500, followed by White engineers ($176,500), Hispanic engineers ($152,178), African-American engineers ($150,000), and Native American/Alaskan Native engineers ($148,000). The salary gap between Black engineers and the average salary reported is $3,500 more than in last year’s report.

Asians and Pacific Islanders are the largest minority group, at 14.4 percent. Only 5 percent of members are Hispanic, 2.6 percent are African Americans, and American Indians/Alaskan Natives account for 0.9 percent of the respondents.

More job satisfaction

According to the report, overall job satisfaction is higher than at any time in the past 10 years. Members reported that their work was technically challenging and meaningful to their company. On the whole, they weren’t satisfied with advancement opportunities or their current compensation, however.

The 60-page report is available for purchase at the member price of US $125. Nonmembers pay $225.

There’s good earnings news for U.S. members: Salaries are rising. Base salaries increased by about 5 percent from 2022 to 2023, according to the IEEE-USA 2024 Salary and Benefits Survey Report.

Last year’s report showed that inflation had outpaced earnings growth but that’s not the case this year.

In current dollars, the median income of U.S. engineers and other tech professionals who are IEEE members was US $174,161 last year, up about 5 percent from $169,000 in 2022, excluding overtime pay, profit sharing, and other supplemental earnings. Unemployment fell to 1.2 percent in this year’s survey, down from 1.4 percent in the previous year.

As with prior surveys, earned income is measured for the year preceding the survey’s date of record—so the 2024 survey reports income earned in 2023.

To calculate the median salary, IEEE-USA considered only respondents who were tech professionals working full time in their primary area of competence—a sample of 4,192 people.

Circuits and device engineers earn the most

Those specializing in circuits and devices earned the highest median income, $196,614, followed by those working in communications ($190,000) and computers/software technology ($181,000).

Specific lucrative subspecialties include broadcast technology ($226,000), image/video ($219,015), and hardware design or hardware support ($215,000).

Engineers in the energy and power engineering field earned the lowest salary: $155,000.

Higher education affects how well one is paid. On average, those with a Ph.D. earned the highest median income: $193,636. Members with a master’s degree in electrical engineering or computer engineering reported a salary of $182,500. Those with a bachelor’s degree in electrical engineering or computer engineering earned a median income of $159,000.

Earning potential also depends on geography within the United States. Respondents in IEEE Region 6 (Western U.S.) fared substantially better than those in Region 4 (Central U.S.), earning nearly $48,500 more on average. However, the report notes, the cost of living in the western part of the country is significantly higher than elsewhere.

The top earners live in California, Maryland, and Oregon, while those earning the least live in Arkansas, Nebraska, and South Carolina.

Academics are among the lowest earners

Full professors earned an average salary of $190,000, associate professors earned $118,000, and assistant professors earned $104,500.

Almost 38 percent of the academics surveyed are full professors, 16.6 percent are associate professors, and 11.6 percent are assistant professors. About 10 percent of respondents hold a nonteaching research appointment. Nearly half (46.8 percent) are tenured, and 10.7 percent are on a tenure track.

Gender and ethnic gaps widen

The gap between women’s and men’s salaries increased. Even considering experience levels, women earned $30,515 less than their male counterparts.

The median primary income is highest among Asian/Pacific Islander technical professionals, at $178,500, followed by White engineers ($176,500), Hispanic engineers ($152,178), African-American engineers ($150,000), and Native American/Alaskan Native engineers ($148,000). The salary gap between Black engineers and the average salary reported is $3,500 more than in last year’s report.

Asians and Pacific Islanders are the largest minority group, at 14.4 percent. Only 5 percent of members are Hispanic, 2.6 percent are African Americans, and American Indians/Alaskan Natives account for 0.9 percent of the respondents.

More job satisfaction

According to the report, overall job satisfaction is higher than at any time in the past 10 years. Members reported that their work was technically challenging and meaningful to their company. On the whole, they weren’t satisfied with advancement opportunities or their current compensation, however.

The 60-page report is available for purchase at the member price of US $125. Nonmembers pay $225.

The IEEE MOVE (Mobile Outreach using Volunteer Engagement) program was launched in 2016 to provide U.S. communities with power and communications capabilities in areas affected by widespread outages due to natural disasters. IEEE MOVE volunteers often collaborate with the American Red Cross.

During the past eight years, the initiative has expanded from one truck based in North Carolina to two, with the second located in Texas. In July IEEE MOVE added a third vehicle, MOVE-3, a van based in San Diego.

IEEE MOVE introduced the new vehicle on 14 August during a ceremony in San Diego. IEEE leaders demonstrated the truck’s modular technology and shared how the components can be transported by plane or helicopter if necessary.

Making MOVE-3 modular

The two other MOVE vehicles are equipped with satellite Internet service, 5G/LTE connectivity, and IP phone service. The trucks can charge up to 100 cellphone batteries simultaneously.

All systems are self-contained, with power generation capability.

“Volunteering is intellectually stimulating. It’s a good opportunity to use your technical knowledge, skills, and abilities.” —Tim Troske

“MOVE-3 has the same technologies but in a modular format so they can be transported easily to remote locations. Unlike the other, larger vehicles, MOVE-3 is a smaller van, which can arrive at disaster sites more quickly,” says IEEE Senior Member Tim Troske, operations lead for the new vehicle. “MOVE-3 has a solar power station that is strong enough to charge two lithium-ion battery packs.”

The vehicle’s flexibility allows the equipment to be deployed not only across California—which is susceptible to wildfires, landslides, and earthquakes—but also to Alaska, Hawaii, and other parts of the Western United States. Similar modular equipment is used by IEEE MOVE programs in Puerto Rico and India.

The new MOVE-3 vehicle was introduced at a ceremony in San Diego. From left: Kathy Hayashi (Region 6 director), Tim Troske (MOVE West operations lead), Loretta Arellano (MOVE USA program director), Kathleen Kramer (IEEE president-elect), Tim Lee (IEEE USA president-elect), Sean Mahoney (American Red Cross Southern California Region CEO) and Bob Birch (American Red Cross local DST manager).IEEE

Become a volunteer

When the vehicles are not deployed for disaster relief, volunteers take them to schools and science fairs to educate students and community members about ways technology can help people during natural disasters.

IEEE MOVE is looking for more volunteers, says IEEE Senior Member Loretta Arellano, MOVE program director, who oversees its U.S. operations.

“Volunteering is intellectually stimulating,” says Troske, who experienced his first emergency deployment in August 2022 after flash floods devastated eastern Kentucky. “It’s a good opportunity to use your technical knowledge, skills, and abilities. You’re at the point of your life where you’ve got all this built-up knowledge and skills. It’s nice to be able to still use them and give back to your community.”

For more information on IEEE MOVE, visit the program’s website. To volunteer, fill out the program’s survey form.

IEEE MOVE is sponsored by IEEE-USA and receives funding from donations to the IEEE Foundation.

Organizations that develop or deploy artificial intelligence systems know that the use of AI entails a diverse array of risks including legal and regulatory consequences, potential reputational damage, and ethical issues such as bias and lack of transparency. They also know that with good governance, they can mitigate the risks and ensure that AI systems are developed and used responsibly. The objectives include ensuring that the systems are fair, transparent, accountable, and beneficial to society.

Even organizations that are striving for responsible AI struggle to evaluate whether they are meeting their goals. That’s why the IEEE-USA AI Policy Committee published “A Flexible Maturity Model for AI Governance Based on the NIST AI Risk Management Framework,” which helps organizations assess and track their progress. The maturity model is based on guidance laid out in the U.S. National Institute of Standards and Technology’s AI Risk Management Framework (RMF) and other NIST documents.

Building on NIST’s work

NIST’s RMF, a well-respected document on AI governance, describes best practices for AI risk management. But the framework does not provide specific guidance on how organizations might evolve toward the best practices it outlines, nor does it suggest how organizations can evaluate the extent to which they’re following the guidelines. Organizations therefore can struggle with questions about how to implement the framework. What’s more, external stakeholders including investors and consumers can find it challenging to use the document to assess the practices of an AI provider.

The new IEEE-USA maturity model complements the RMF, enabling organizations to determine their stage along their responsible AI governance journey, track their progress, and create a road map for improvement. Maturity models are tools for measuring an organization’s degree of engagement or compliance with a technical standard and its ability to continuously improve in a particular discipline. Organizations have used the models since the 1980a to help them assess and develop complex capabilities.

The framework’s activities are built around the RMF’s four pillars, which enable dialogue, understanding, and activities to manage AI risks and responsibility in developing trustworthy AI systems. The pillars are:

• Map: The context is recognized, and risks relating to the context are identified.
• Measure: Identified risks are assessed, analyzed, or tracked.
• Manage: Risks are prioritized and acted upon based on a projected impact.
• Govern: A culture of risk management is cultivated and present.

A flexible questionnaire

The foundation of the IEEE-USA maturity model is a flexible questionnaire based on the RMF. The questionnaire has a list of statements, each of which covers one or more of the recommended RMF activities. For example, one statement is: “We evaluate and document bias and fairness issues caused by our AI systems.” The statements focus on concrete, verifiable actions that companies can perform while avoiding general and abstract statements such as “Our AI systems are fair.”

The statements are organized into topics that align with the RFM’s pillars. Topics, in turn, are organized into the stages of the AI development life cycle, as described in the RMF: planning and design, data collection and model building, and deployment. An evaluator who’s assessing an AI system at a particular stage can easily examine only the relevant topics.

Scoring guidelines

The maturity model includes these scoring guidelines, which reflect the ideals set out in the RMF:

• Robustness, extending from ad-hoc to systematic implementation of the activities.
• Coverage, ranging from engaging in none of the activities to engaging in all of them.
• Input diversity, ranging from having activities informed by inputs from a single team to diverse input from internal and external stakeholders.

Evaluators can choose to assess individual statements or larger topics, thus controlling the level of granularity of the assessment. In addition, the evaluators are meant to provide documentary evidence to explain their assigned scores. The evidence can include internal company documents such as procedure manuals, as well as annual reports, news articles, and other external material.

After scoring individual statements or topics, evaluators aggregate the results to get an overall score. The maturity model allows for flexibility, depending on the evaluator’s interests. For example, scores can be aggregated by the NIST pillars, producing scores for the “map,” “measure,” “manage,” and “govern” functions.

When used internally, the maturity model can help organizations determine where they stand on responsible AI and can identify steps to improve their governance.

The aggregation can expose systematic weaknesses in an organization’s approach to AI responsibility. If a company’s score is high for “govern” activities but low for the other pillars, for example, it might be creating sound policies that aren’t being implemented.

Another option for scoring is to aggregate the numbers by some of the dimensions of AI responsibility highlighted in the RMF: performance, fairness, privacy, ecology, transparency, security, explainability, safety, and third-party (intellectual property and copyright). This aggregation method can help determine if organizations are ignoring certain issues. Some organizations, for example, might boast about their AI responsibility based on their activity in a handful of risk areas while ignoring other categories.

A road toward better decision-making

When used internally, the maturity model can help organizations determine where they stand on responsible AI and can identify steps to improve their governance. The model enables companies to set goals and track their progress through repeated evaluations. Investors, buyers, consumers, and other external stakeholders can employ the model to inform decisions about the company and its products.

When used by internal or external stakeholders, the new IEEE-USA maturity model can complement the NIST AI RMF and help track an organization’s progress along the path of responsible governance.