Image

Associate Professor of Economics Veronika Dolar, PhD, is an economist whose research spans labor, health, and sports economics, exploring how deeply structural inequalities shape opportunity—whether in the realm of Olympic performance or everyday life.

As a dedicated faculty member in Pace’s award-winning Economics department, she integrates real-world data and research into both her teaching and the mentoring of undergraduate students, empowering them to produce their own publishable work.

Economics and the Olympic Dream: A Quest to Make Data Human

Dolar’s recent work explored how income inequality affects national performance at the Winter Olympic Games. Her findings are both striking and sobering: nations with greater income inequality consistently send smaller delegations and win fewer medals, even after accounting for GDP, population, and institutional factors.

“While anyone can, in principle, qualify for the Olympics, the cost of elite training, coaching, equipment, and facilities make participation inaccessible to many,” she explained.

Using data from every Winter Games between 1992 and 2022, Dolar’s research treats Olympic success as an indicator of how effectively societies convert human capital into achievement. She has isolated the causal impact of inequality on performance, offering a fresh bridge between macroeconomic theory and sports analytics.

Through her research, presented at conferences from the Eastern Economic Association to the Center for Sociocultural Sport and Olympic Research, Dolar has created interactive data visualization tools, accessible to policymakers and journalists, but also students at Pace.

Teaching Economics Through Real-world Stories

Dolar’s classroom reflects the same passion for connection between theory and practice and illuminates how economic structures shape opportunity in all areas of life.

Her economic inequality course, for example, integrates her own research, allowing students to work directly with the same datasets she uses in her publications. Students then become researchers in the process, replicating analyses using real data—from the World Bank to the Standardized World Income Inequality Database—and extend the work to areas such as education and healthcare.

“Economics isn’t just about money or markets,” she emphasized. “It’s about human potential and fairness—who gets to compete and succeed.”

Mentoring Through Research and Collaboration

Dolar also models the power of collaboration through student research in other ways.

An example is her partnership with undergraduate students, such as Fatima Abba ’26, to co-develop a manuscript inspired by Robert Reich’s Wealth & Poverty lecture series. What began as lecture summaries evolved into a project blending theory, data, and narrative to explore global inequality. Supported by the Dyson Student-Faculty Summer Research Award and Omicron Delta Epsilon, it will serve as a foundation for Dolar’s future textbook, Understanding Economic Inequality: An Introductory Guide Through Real-World Economics.

“It was transformative,” she reflected. “The students gained hands-on experience with data, writing, and policy analysis. It showed them that research can become real scholarship.”

In addition, in her fall 2025 introduction to macroeconomics class, an assignment was included, as part of the Archipelago Macroeconomics Project, that is a reflection of students shifting from being passive recipients of traditional economic knowledge to active producers whose analyses become accessible to more broader, public audiences.

Working in teams, and under Dolar’s guidance, they created websites and digital artifacts that analyzed core macroeconomic indicators for Caribbean island economies, comparing them to the US, New York State, and New York City. The result: Pace students with more academic agency, ownership, and collaboration in an otherwise large lecture course.

Reimagining How Economics Is Taught

Image

In recent years, Dolar has also taken on the challenge of modernizing and reshaping how economics is taught in classrooms at Pace and beyond, and with that, a chance to make economics engaging and relevant to a new generation of students.

To this end, she has co-authored the 7th editions of ECON MACRO and ECON MICRO (Cengage Learning, 2024), texts that connect theory to everyday examples—from the competition between Ben & Jerry’s and Häagen-Dazs to the recent spike in egg prices.

“Economics, especially macroeconomics, has changed dramatically,” she said. “And our textbooks need to reflect that reality.”

In addition, Dolar has illuminated the continuing exclusion and underrepresentation of women within the economics profession (a trend, by the way, that Pace is bucking) through co-authoring another work, Missing Voices in Economics: Addressing the Gender Gap (Palgrave MacMillan, 2026). The book has been utilized in her Pace course on Economics of Gender, Race, and Class, integrating its findings into classroom discussion and analysis.

The Heart of Her Work: Making a Difference

Whether in her research, authorship of textbooks, or mentorship of students, Dolar’s passion is clear: helping others understand the world so they can make it better.

“What motivates me is seeing that ‘aha’ moment when students realize economics can explain the forces shaping their lives. That curiosity and empowerment are what make this work so rewarding,” she said.

Elisabeth Haub School of Law at Pace University’s Alternative Dispute Resolution (ADR) Team earned top honors at the New York Law School NIL and College Sports Negotiation Competition, held March 7–8, with one Pace Haub Law team winning the championship and the other finishing as runner-up.

The competition, which featured 14 teams, focused on the rapidly evolving legal landscape surrounding Name, Image, and Likeness (NIL) rights in college athletics. Competing teams negotiated complex agreements involving athlete endorsements, sponsorship deals, and university partnerships, with judges drawn from attorneys and professionals working in the NIL and sports law fields.

Pace Haub Law was represented by two ADR teams. Rebecca Ferguson (3L) and Zach Ouladelhadjahmed (2L) emerged as competition champions, while Alexa Stack (2L) and Asly Abbas (2L) advanced to the final round and finished as runner-up. The teams were coached by alumna Andrea Garcia ‘24 and student coach Madeline M. Law (3L), who currently serves as Executive Productions Editor of Pace Law Review and Alternative Dispute Resolution Director for the Pace Law Advocacy Program.

“This was a challenging and unique competition,” said student coach Madeline Law (3L). “I was thrilled to assist the teams as a coach, which gave me a new perspective compared to my usual role as a competitor. I am incredibly proud of both teams for their preparation, professionalism, and ability to think creatively under pressure.”

Louis Fasulo, Professor of Trial Practice and Director of Advocacy Programs at Pace Haub Law, praised the students’ performance and the continued growth of the Law School’s advocacy and ADR programs. “Our students continue to demonstrate exceptional skill and professionalism in national advocacy competitions,” Professor Fasulo said. “This result reflects the depth of talent within our ADR team and the dedication of the students and coaches who work tirelessly to prepare. Having two Pace teams reach the final round speaks volumes about the strength of our program and the experiential learning opportunities we provide.”

The competition is designed to give law students hands-on experience negotiating agreements in the increasingly complex world of collegiate athletics and NIL regulations. For Pace Haub Law students, the victory highlights the continued success of the Law School’s Advocacy Program and ADR Team, which regularly compete and place in national competitions. With both teams advancing to the final round and securing first and second place, the event marked another significant achievement for Pace Haub Law’s advocacy students and coaches.

Students connected with 100+ employers—including EY, KPMG, FUJIFILM, and Northwell Health

Hundreds of Pace University students packed the Spring 2026 Job and Internship Career Fair on Wednesday —including many from Pace’s campus in Lower Manhattan— eager to connect with more than 100 top employers across a wide range of industries.

Students met face-to-face with recruiters, explored career paths, and expanded their professional networks during one of Career Services’ signature events of the semester.

Image

Employers in attendance included Americare, BDO USA, EY, FUJIFILM, ICON International, KPMG, Metropolitan Transportation Authority (MTA), Northwell Health, New York Boulders, Office of the New York State Comptroller, Cisco among many others.

“Career fairs like this give students the chance to move beyond the classroom and engage directly with employers,” said Phyllis Mooney, assistant vice president of Career Services and Employer Relations. “Those conversations can open doors to internships, mentorship, and ultimately full-time careers.”

Career Services continues to deliver outcomes that position Pace as a national leader in career development. In 2025, over 9,000 internships, fieldwork/clinical placements and similar experiences were completed by Pace students. More than 14,000 employers posted 140,000+ jobs and internships for students through the University’s career platform.

In fact, the Class of 2024 achieved a 95% employment or continuing education rate within six months of graduation— including 93% of bachelor’s and 97% of master’s degree graduates—with the majority working in fields related to their studies.

“As a junior who transferred to Pace this spring from West Point, I came to the fair to learn more about the industries and companies here in New York,” said Matthew Adoghe ’27, a Business Economics major and member of the Pace football team, from Atlanta, Ga. “It was a great opportunity to get my name out there and connect with organizations I hadn’t originally considered.”

Pace graduates’ success is also reflected in salary outcomes: the average full-time salary for 2024 bachelor’s graduates was $75,098, and $87,153 for master’s graduates. Over the last five years, employment rates for Pace graduates have remained 10 percentage points higher than the national average, according to the National Association of Colleges and Employers First Destination Survey. Additionally, Pace ranks in the top 9 percent of private U.S. colleges for return on tuition investment and in the top 11 percent of U.S. colleges with the highest-earning alumni, according to PayScale.

Pace’s Career Services also offers their employer partners a tailored, successful recruiting experience that introduces recruiters to talented students that represent the very best of Westchester, the New York City region, and the world—resulting in extraordinary outcomes. Top companies that employ Pace students include ABC News, Bank of America, BNY Mellon, Cisco, Citi, Deloitte, EY, IBM, KPMG, Live Nation Entertainment, Memorial Sloan Kettering Cancer Center, Montefiore, Morgan Stanley, NBCUniversal, PwC, UBS and Warner Bros Discovery.

“This is my first career fair, so I’m really excited to see the range of companies here,” said Elizabeth Odisote ’26, a graduate student in Public Accounting, who was among students from Pace’s Lower Manhattan campus who attended the job fair. “It’s helpful to talk directly with recruiters and get a better sense of what they’re looking for.”

About Pace University

Founded in 1906 and celebrating 120 years of preparing students for success in 2026, Pace University pairs real-life learning with strong academics to launch meaningful careers. With campuses in New York City and Westchester County, Pace serves 13,600 students across a range of bachelor, master, and doctoral programs through the College of Health Professions, Dyson College of Arts and Sciences, Elisabeth Haub School of Law, Lubin School of Business, Sands College of Performing Arts, School of Education, and Seidenberg School of Computer Science and Information Systems.

The Elisabeth Haub School of Law at Pace University once again co-hosted the New Directions in Environmental Law (NDEL) Conference, held February 7, 2026, at Yale Law School. This year’s conference, themed “Collective Reimagining: Channeling Conviction into Change,” brought together scholars, practitioners, and students to explore emerging challenges and innovative approaches in environmental law.

Founded in 2011, students have organized NDEL to examine changes in and novel approaches to environmental law, highlight innovative scholarship, interdisciplinary perspectives, and collaboration across institutions. Since its inception, Pace Haub Law has played an active role in the event. In 2021, the Law School became an official co-host of the event, with a Pace Haub Law student serving as a conference co-chair each year since. As home to the #1 ranked environmental law program, Haub Law remains committed to driving the future of environmental law and policy, and its continued role in co-hosting NDEL highlights its position as a leader in environmental legal education.

This year’s conference featured strong engagement from the Pace Haub Law community. Professor Josh Galperin and Professor Camila Bustos served as faculty advisors for the event, supporting students in organizing and coordinating the conference. Professor Galperin also delivered the conference’s closing remarks, while Professor Bustos participated in two panels.

“This conference is a remarkable example of what students can accomplish when they bring together creativity, scholarship, and collaboration,” said Professor Josh Galperin. “NDEL creates space for emerging scholars and practitioners to engage deeply with the environmental challenges of our time while building a community dedicated to innovative solutions.”

Image

Several Pace Haub Law faculty members contributed their expertise as panelists, including Professor Smita Narula and Professor Achinthi Vithanage. The conference also welcomed Professor Doug Kysar, who will visit Pace Haub Law as a Haub Visiting Scholar this spring, and James Van Nostrand, a Pace Haub Law LLM alumnus and former Executive Director of the Pace Energy and Climate Center, as presenters. Rachel Rothschild, who received the 2025–2026 Pace Haub Environmental Law Distinguished Junior Scholar Award, delivered the keynote address.

Pace Haub Law students played a central role in organizing and shaping the conference. Ryan York and Julie Frey served as co-chairs of the 2026 NDEL Conference, helping to coordinate the event and manage the Law School delegation. Students also contributed to the conference program by developing and moderating panels. Pace Haub Law student Ashley Gentile conceived and moderated a panel, while Eli Calhoun conceived and moderated another, with LLM student Joe Mayson also conceiving two panels featured at the conference. Several students involved in the NDEL conference are either currently enrolled in or have been accepted to the joint degree program between the Elisabeth Haub School of Law and the Yale School of the Environment, which enables participants to earn a JD from Pace Haub Law and a master’s degree from Yale.

“Helping organize NDEL was an incredible opportunity to work with students and scholars from across the country who are passionate about environmental law,” said Pace Haub Law student Ryan York. “Seeing the ideas generated in the panels, research presentations, and discussions throughout the day made clear how important it is for the next generation of environmental lawyers to collaborate and think creatively about solutions.”

Students further showcased their research through the conference’s poster session. Poster presenters included students Ayman Irfan, Jamal Fofanah, Krissadele Michael, Colin Schmitt, and Nicholas Caiciedo. Two of the student presenters, Ayman Irfan and Colin Schmitt, notably both submitted papers based on their poster research to law review journals, and both papers were accepted for publication.

Image

In total, 35 Pace students attended this year’s conference, serving as student leaders, NDEL coordinators, student reports, presenting research, developing and moderating panels, and contributing as general conference volunteers. This impressive amount of student involvement reflects the breadth of student involvement from the Pace Haub Law community. Through active participation in organizing panels, presenting scholarship, and contributing to conference reporting, Pace Haub Law students and faculty continue to play a significant role in advancing dialogue on the future of environmental law through the New Directions in Environmental Law Conference.

If you’re thinking about graduate school for software development you’ve probably had the same nagging thought as a lot of working developers and career changers: Am I investing in a field that AI is about to shrink? It’s a fair concern, especially when headlines make it sound like code writes itself and whole applications appear from a prompt.

Software development careers have always been shaped by moments like this, when new capabilities force the field to reconsider what expertise actually looks like. AI introduces that kind of moment by shifting where skill and responsibility show up in the work, which makes questions about education, career paths, and long-term value worth examining.

“The most significant change in AI is the ease of access to Gen AI tools to regular users,” says Interim Dean Li-Chiou Chen of Pace’s Seidenberg School of Computer Science and Information Systems. “That is, everyone with internet access can use AI tools to some extent. Therefore, AI is impacting every aspect of our lives and is helping us make decisions in many areas, such as healthcare, communications, education, legal, marketing, and more.”

That accessibility is reshaping what it means to work in software. To understand where the field is headed, it helps to look at how AI is already changing day-to-day development work.

How Has AI Helped Transform the Role of Software Developers?

AI has changed how work is distributed across projects. Tasks that used to absorb large amounts of time now move faster, which shifts how engineers approach design, implementation, and review.

In day-to-day workflows, AI-assisted systems often support routine development work by:

• Generating first drafts of code that can be reviewed and refined
• Reducing time spent on boilerplate setup and common patterns
• Suggesting fixes or improvements during implementation
• Producing images, icons, and visual assets from text descriptions

Testing and quality assurance have shifted in similar ways. AI systems can analyze codebases for patterns associated with defects or failures, which changes how teams approach coverage and validation.

Common uses include:

• Automatically generating test cases from existing code
• Surfacing edge cases that are easy to miss in manual testing
• Flagging higher-risk areas earlier in the development cycle

Security and reliability benefit from related analysis. AI-driven scanning can flag anomalies and potential vulnerabilities, helping teams focus review time where it matters most. These systems don’t replace secure coding practices, but they can help teams catch issues sooner, especially in large codebases.

Dr. Christelle Scharff, professor of computer science and co-director of the Pace AI Lab, sees this shift as part of a broader pattern, explaining, “Everybody will have to be able to use AI and this will be a differentiator. Large Language Models and image generators will permit professionals to be more productive and be used as supportive tools in all possible sectors.”

Across these use cases, AI primarily handles pattern recognition and repetition. Developers remain responsible for correctness, tradeoffs, and ensuring systems behave as expected in production.

How AI Helps Increase Efficiency Across the Software Development Process

AI’s impact shows up most clearly through efficiency gains. By reducing friction in common tasks, AI-assisted systems can help teams maintain momentum across planning, building, and review. In one GitHub survey, 70 percent of developers said AI coding tools would give them an advantage at work.

Early-stage development is where many teams feel the difference first. AI tools are often used to get features or components off the ground faster, including:

• Producing initial code drafts that can be adapted to project needs
• Filling in standard patterns without manual setup
• Moving from idea to working example more efficiently

AI support can extend into design and interface work at a basic level. When tools generate initial layouts or page structures, designers and engineers can focus more attention on interaction, usability, and refinement instead of building everything from scratch.

As projects grow, efficiency gains continue through analysis and maintenance. AI-driven scanning can help identify duplicated logic, performance issues, or refactoring opportunities across large codebases. Instead of searching manually, teams receive targeted suggestions to support maintainability work.

Natural language interfaces can speed up implementation, too. Developers describe desired behavior in plain language and receive relevant code suggestions, reducing context switching. Related techniques assist with debugging by flagging likely sources of errors and prioritizing issues based on past patterns.

In collaborative environments, these efficiencies can support steadier delivery. AI tools may help draft pull requests, summarize changes, and support reviews, all of which are useful when code volume increases and review bandwidth gets tight.

5 AI Tools That Help Software Developers

AI tools are now part of many standard development environments. Each tends to support a specific part of the workflow, and their value depends on fit and oversight.

1. GitHub Copilot

Developed by GitHub in collaboration with OpenAI, GitHub Copilot provides inline code suggestions based on context within an editor.

• How it helps:
  • Generates drafts of functions and common patterns
  • Reduces time spent on boilerplate and repetitive syntax
  • Supports multiple languages and frameworks
• Pros:
  • Deep integration with popular IDEs
  • Useful for accelerating routine coding tasks
• Cons:
  • Output requires careful review
  • Less effective in highly domain-specific codebases

2. Claude AI

Claude AI, developed by Anthropic, is a conversational AI assistant that supports software development through code generation, explanation, debugging, and document analysis.

• How it helps:
  • Generates, reviews, and explains code across multiple languages
  • Assists with architecture decisions, documentation, and technical writing
  • Handles long-context inputs, which is useful for analyzing large files or complex requirements
• Pros:
  • Strong reasoning and explanation capabilities
  • Useful for both code tasks and broader technical problem-solving
• Cons:
  • Operates as a conversational tool rather than an IDE plugin
  • Responses benefit from clear, specific prompts for technical tasks

3. Amazon CodeWhisperer

Amazon CodeWhisperer provides real-time code suggestions inside IDEs, with an emphasis on cloud-based development.

• How it helps:
  • Assists with writing cloud services and integrations
  • Flags potential security issues during development
• Pros:
  • Strong support for AWS workflows
  • Built-in security scanning
• Cons:
  • Suited primarily for teams using AWS
  • Less flexible for non-cloud projects

4. Perplexity

Perplexity is an AI-powered research and answer engine that combines large language model capabilities with real-time web search, which makes it useful for developers who need up-to-date technical information.

• How it helps:
  • Quickly surfaces documentation, tutorials, and technical references with source citations
  • Answers implementation questions with current information rather than relying on static training data
  • Supports research on unfamiliar libraries, APIs, or frameworks
• Pros:
  • Combines conversational AI with live web search for current results
  • Cites sources directly, making it easier to verify information
• Cons:
  • Not designed for direct code generation inside an IDE
  • Output depends on the quality and availability of indexed sources

5. Qodo

Qodo (formerly CodiumAI) is an AI-powered code review tool that analyzes changes in real time to identify bugs, logic issues, and quality concerns before code is merged.

• How it helps:
  • Flags potential errors and inconsistencies during development
  • Reviews pull requests to surface higher-risk changes
  • Supports automated quality checks across codebases
• Pros:
  • Improves code quality early in the workflow
  • Useful for scaling review across larger projects
• Cons:
  • Requires configuration to match team standards
  • Still depends on human review for final decisions

What Are the Risks of Using AI in Software Development?

AI tools can improve speed and reduce friction, but they also introduce risks that teams need to manage deliberately, especially when tools are used without clear limits or oversight.

One set of risks involves data quality and reliability. AI systems depend on large training datasets, and output quality often mirrors dataset quality.

Common challenges include:

• Time and cost associated with sourcing, cleaning, and maintaining datasets
• Inaccurate suggestions when training data doesn’t reflect real-world conditions
• Gaps around edge cases that fall outside common patterns

There are also limits to how well AI handles unfamiliar or complex situations. These tools perform best with well-defined tasks but can struggle in systems with tightly coupled components or highly specific business rules.

Transparency is another concern. AI-generated code doesn’t always make its reasoning clear, which can make inefficient logic or subtle errors harder to detect during review, especially when output looks superficially correct.

Operational dependency can introduce risk as well. Teams that build workflows around constant AI availability may see slowdowns if access changes, policies shift, or tools become unavailable.

Privacy and security remain ongoing considerations. Many AI systems process large volumes of data, raising questions around consent, sensitive information, and compliance. Teams typically address this through access controls, anonymization, encryption, and clear internal usage policies.

Dr. Soheyla Amirian, assistant professor of computer science at Seidenberg, frames the tension this way: “The opportunity lies in AI’s potential to revolutionize industries, improve efficiency, and solve global challenges. But it becomes a threat if not used ethically and responsibly. By fostering ethical frameworks and interdisciplinary collaboration, we can ensure AI serves as a tool for good, promoting equity and trust.”

Will We Still Need Software Developers in the Future?

As AI tools become more capable, questions about the future of software development are unavoidable. While AI can assist with implementation, several core responsibilities continue to depend on human expertise.

Areas where software developers remain essential include:

• System design and architecture. Developers make decisions about structure, scalability, integration, and long-term maintainability, i.e., work that requires understanding constraints and downstream impact.
• Context and continuity. Long-lived codebases reflect years of decisions and tradeoffs. Developers provide the historical and situational awareness needed to make safe changes.
• Review and refinement. AI-generated output often needs adjustment to meet performance expectations, security requirements, and team standards.
• Collaboration and problem-solving. Software development requires clarifying requirements, resolving ambiguity, and coordinating across roles. That work shapes what gets built and how it functions in practice.

Brian McKernan, PhD, assistant professor of communication and media studies at Pace’s Dyson College of Arts and Sciences, puts it simply, “There are great uses for AI, particularly in cases with huge amounts of data. But we will always need humans involved in verifying.”

Dr. James Brusseau, a Dyson faculty member and close collaborator with Seidenberg, echoes that perspective, stating, “AI, more than anything else, is just a tool. That is, I do not think it is capable of producing thought as you and I do.”

As AI takes on more routine programming tasks, the developer role shifts toward guiding decisions, evaluating outcomes, and staying accountable for what ships.

Skills Needed as a Software Developer in the Age of AI

With AI becoming a standard part of software development workflows, expectations for developers continue to expand. Strong programming foundations are still important, but today’s most effective engineers also combine AI literacy, data skills, cloud expertise, and the ability to collaborate and make strategic decisions.

Generative AI can accelerate coding and testing tasks, but it does not replace the need for developers who can design systems, evaluate risks, and guide projects from concept to deployment. That shift explains why technical depth, interpersonal skills, and decision-making are all becoming more important in AI-driven environments.

Gartner has projected that through 2027, generative AI will create new roles in software engineering and operations, requiring 80 percent of the engineering workforce to upskill.

Dr. Christelle Scharff reinforces this point, adding, “I believe that the big deal is that the tools related to AI are now in the hands of everybody, while earlier advances were confined and used by people in tech.”

Technical Skills

Modern software developers working with AI need a broad technical foundation that spans programming, data, infrastructure, and responsible system design.

• Programming and development:
  • Proficiency in languages such as Python, R, Java, C++, or JavaScript
  • Familiarity with AI frameworks and libraries like TensorFlow, PyTorch, Scikit-learn, and Keras
  • Strong understanding of object-oriented programming and data structures
• AI and machine learning:
  • Understanding of supervised, unsupervised, and reinforcement learning approaches
  • Experience with natural language processing and computer vision applications
• Data skills:
  • Ability to clean, analyze, and preprocess large datasets
  • Experience working with structured and unstructured data
  • Familiarity with databases and big data tools such as SQL, NoSQL, Hadoop, or Spark
• Cloud and DevOps:
  • Experience with cloud platforms such as AWS, Google Cloud, or Azure
  • Knowledge of DevOps practices and CI/CD pipelines for deploying and maintaining applications
• AI ethics and security:
  • Understanding of AI bias, privacy concerns, and regulatory requirements
  • Skills in designing secure and compliant AI-enabled systems

Interpersonal Skills

As AI automates portions of coding and testing, developers spend more time collaborating, translating requirements, and shaping how systems are built.

• Communication and collaboration:
  • Explaining technical concepts to non-technical stakeholders
  • Working effectively in cross-functional teams with product managers, designers, and data scientists
• Teamwork and knowledge sharing:
  • Mentoring junior developers and reviewing code
  • Contributing to shared standards and best practices
• Adaptability:
  • Staying current with emerging tools and frameworks
  • Adjusting quickly as project goals and technologies evolve

Decision-Making Skills

AI increases both the speed of development and the complexity of technical choices. Developers are increasingly responsible for guiding projects strategically and managing risk.

• Strategic thinking:
  • Aligning AI initiatives with business objectives
  • Identifying high-impact opportunities for AI integration
• Project management:
  • Planning timelines, resources, and deliverables
  • Using tools such as Jira, Trello, or Asana to track progress
• Risk management:
  • Anticipating technical, ethical, and operational risks
  • Ensuring compliance with security standards and regulatory requirements

These skill areas reflect how software development roles are changing alongside AI, where strong technical foundations are paired with collaboration, judgment, and system-level thinking.

6 In-Demand Software Developer Jobs That Use AI

Many roles now require AI experience. Some of the fastest-growing and highest-paying positions combine strong engineering foundations with experience building, integrating, or operating AI-enabled systems.

According to the U.S. Bureau of Labor Statistics (BLS), overall employment in computer and information technology occupations is projected to grow much faster than the average for all occupations from 2024 to 2034, with about 317,700 openings projected each year. Roles such as software developer and data scientist are among the occupations driving that momentum.

1. Machine Learning Engineer

Machine learning engineers build software systems that train, deploy, and monitor models in production. They work closely with application developers to integrate machine learning into real products and services.

• Typical education: Bachelor’s in computer science, software engineering, or related field; master’s often preferred
• Key skills: Python or Java, machine learning frameworks, data pipelines, model evaluation, deployment
• Salary (NYC): $136,000–$219,000 per year (median $172,000)
• Job outlook: 34 percent growth through 2034, much faster than average

2. AI Engineer

AI engineers design and implement AI-powered features inside applications, such as recommendation systems, automation tools, and intelligent interfaces. Their work connects models to APIs, cloud infrastructure, and user-facing systems within development workflows.

• Typical education: Bachelor’s in computer science or engineering; graduate study increasingly common
• Key skills: Software development, APIs, cloud platforms, AI model integration
• Salary (NYC): $135,000–$227,000 per year (median $174,000)
• Job outlook: 15 percent growth through 2034, well above average

3. Data Scientist

Data scientists build analytical pipelines and machine learning models that support software products and data-driven platforms. They collaborate closely with software developers to move models from experimentation into production.

• Typical education: Bachelor’s in a quantitative field; master’s often preferred
• Key skills: Statistics, machine learning, SQL, Python or R, data visualization
• Salary (NYC): $127,000–$217,000 per year (median $165,000)
• Job outlook: 34 percent growth through 2034, among the fastest-growing tech roles

4. AI Research Scientist

AI research scientists develop new algorithms and learning approaches that later become part of commercial software systems, platforms, and developer tools.

• Typical education: Master’s degree or PhD in AI, computer science, or related field
• Key skills: Advanced mathematics, ML theory, experimentation, research
• Salary (NYC): $165,000–$256,000 per year (median $204,000)
• Job outlook: 20 percent growth through 2034, significantly faster than average

5. Robotics Engineer

Robotics engineers write and maintain the software that controls automated systems, sensors, and intelligent machines. They combine software development with AI-driven perception and control to operate in physical environments.

• Typical education: Bachelor’s in engineering or computer science; advanced degrees common
• Key skills: Embedded systems, controls, AI integration, hardware/software coordination
• Salary (NYC): $110,000–$184,000 per year (median $141,000)
• Job outlook: 9 percent growth through 2034, faster than average

6. AI Product Manager

AI product managers guide the development of AI-powered software products, translating business goals into technical requirements and working with engineering teams through build, testing, and deployment.

• Typical education: Bachelor’s degree; many roles favor a master’s in technology or business
• Key skills: Product strategy, technical communication, AI fundamentals, stakeholder coordination
• Salary (NYC): $173,000–$262,000 per year (median $211,000)
• Job outlook: 15 percent growth through 2034, showing strong demand across tech leadership roles

Bonus: Software Developers (General)

Software developers design, build, test, and maintain applications and systems across industries, increasingly incorporating AI tools, automation, and machine learning into modern software architectures.

• Typical education: Bachelor’s in computer science or related field
• Key skills: Programming, system design, debugging, collaboration
• Salary (NYC): $108,000–$178,000 per year (median $138,000)
• Job outlook: 15 percent growth through 2034, much faster than average

Note: Salary figures are based on Glassdoor data collected in January 2026. Actual compensation may vary over time and can fluctuate based on factors such as location, employer, experience level, industry, and the number of reported salary submissions.

Overall Job Outlook for AI-Driven Software Careers

Employment across computer and information technology occupations is projected to grow much faster than average over the next decade. Expanding use of artificial intelligence, cloud computing, automation, cybersecurity, and data-driven systems continues to drive demand for professionals who can design, build, and manage modern software environments.

For a closer look at career options in AI-related fields, Pace’s program pages for the MS in Artificial Intelligence, MS in Applied Artificial Intelligence, and BS in Artificial Intelligence include detailed career outcome information.

FAQ

What is AI in software development?
AI in software development refers to using machine learning systems to support tasks such as writing code, testing software, debugging, and reviewing changes. These tools analyze patterns in existing code and data, while developers remain responsible for design decisions, validation, and deployment.

How can AI be used in software development?
Common uses of AI in software development include generating code drafts, creating or expanding test coverage, spotting potential bugs, generating images and visual assets, flagging security risks, summarizing changes for reviews, and supporting debugging workflows.

What is an AI software developer?
An AI software developer is a developer who builds, integrates, or works alongside AI-driven systems. This can include developing machine learning models, embedding AI services into applications, or using AI tools to support development workflows.

Is AI replacing software developers?
AI is not replacing software developers. It is changing how development work is distributed by handling routine tasks and generating drafts, while developers continue to guide design, review output, and manage complexity in production systems.

Can AI do coding?
AI can generate, refactor, and suggest code based on learned patterns. It does not independently understand business requirements, system constraints, or long-term impact. Developers must review and adapt AI-generated code to ensure it works correctly within a broader system.

What jobs will AI replace?
AI is more likely to automate specific tasks rather than fully replace most technology roles. Repetitive, narrow-scope work such as basic code translation, routine testing, or simple script generation is increasingly handled by AI tools. However, roles that involve system design, problem-solving, collaboration, and oversight continue to grow as organizations adopt AI across software development.

Which jobs will not be replaced by AI?
Roles that require system-level thinking, human judgment, and accountability are far less likely to be fully replaced by AI. This includes software architects, senior developers, security and infrastructure leaders, platform engineers, and technical product roles that guide design decisions, manage risk, and take responsibility for real-world system performance.

Looking Ahead

AI has become part of how software is built, but it hasn’t removed the need for people who can design systems, make decisions, and take responsibility for outcomes. For students weighing graduate education, preparation focuses on building those capabilities alongside technical depth.

Pace’s Seidenberg School has been teaching and researching AI for over 30 years. Faculty like Dr. Christelle Scharff, co-director of the Pace AI Lab, and Dr. Soheyla Amirian lead research across healthcare AI, computer vision, and ethical AI frameworks. Students work alongside these researchers through lab projects, hackathons, and industry partnerships. To learn more, read about Seidenberg’s legacy of AI excellence or explore how faculty are powering student discovery.

To explore how Pace approaches that preparation, you can review the Master of Science in Software Development and Engineering program options:

• MS in Software Development and Engineering, on-campus format
• MS in Software Development and Engineering, online format

If you’d like to talk with an admissions counselor about fit, timelines, and prerequisites, you can also request information anytime.