Software Engineer Education, Degrees & How AI Is Changing It
By
Liz Fujiwara
•
Jan 16, 2026
The U.S. Bureau of Labor Statistics projects 25 percent job growth for software developers from 2022 to 2032, well above average. At the same time, AI tools like GitHub Copilot and ChatGPT have changed what employers expect from new engineers, weakening the idea that a four-year computer science degree alone signals job readiness.
Today, candidates enter software engineering through many paths, including traditional degrees, bootcamps, online programs, and self-directed learning. Nearly 45 percent of companies planned to remove bachelor’s degree requirements for some technical roles by 2024.
For founders and hiring managers, filtering only on degrees risks missing strong talent. Skill-based, AI-assisted evaluation is becoming essential, which is where Fonzi fits by assessing real-world engineering ability rather than credentials.
Key Takeaways
Most software engineers still come from computer science or related STEM degrees, but many enter the field through bootcamps or self-study, where employers focus more on portfolios, real-world experience, and technical assessment performance than formal credentials.
AI in 2026 is reshaping both required skills, such as LLMs and MLOps, and hiring evaluation, shifting emphasis from degrees to demonstrated capability.
Fonzi supports this shift by continuously assessing engineers on realistic, production-like tasks, helping founders and CTOs hire elite AI engineers in about three weeks regardless of educational background.
What Education Do You Need to Become a Software Engineer?
In 2026, most software engineer job descriptions list a bachelor’s degree in computer science, software engineering, or a related field as “required or strongly preferred.” However, employers are increasingly flexible when candidates can prove their skills through portfolios, projects, and structured assessments.
Common entry-level requirements include:
Bachelor’s degree (B.S.) in CS, SE, or a related STEM field
0–2 years of professional experience or substantial internship experience
Proficiency in at least one modern programming language such as Python, JavaScript, Java, C#, or Go
Foundational knowledge of data structures, algorithms, and operating systems
Familiarity with version control (Git), databases, and core software development practices
The Bureau of Labor Statistics notes that software developers and quality assurance analysts typically need a bachelor’s degree in computer and information technology or a related field. Large employers like Google, Microsoft, and Meta still lean toward degree holders but have publicly stated openness to exceptional bootcamp or self-taught candidates with strong portfolios.
Certain subfields maintain stricter requirements. Embedded systems, critical infrastructure, aerospace, and security roles in regulated industries often favor ABET-accredited engineering degrees. In contrast, web development, product engineering, and many startup environments are more flexible about educational background.
Guidance for founders and CTOs: treat degrees as a useful signal, not a hard filter. In practice, the strongest predictor of success is often what candidates have actually built and shipped.
Types of Degrees for Aspiring Software Engineers
There’s no single “right” degree for becoming a software engineer. Different programs emphasize different foundations, such as theory versus engineering practice, hardware versus software, or pure computer science versus business-oriented IT. What matters is how well the education prepares someone to build reliable software systems.
This section walks through the major degree types (associate, bachelor’s, master’s, PhD) and the most common majors that lead to software engineering roles. The focus reflects 2026 realities, including greater AI integration, more remote work, and a stronger emphasis on practical project experience within degree programs.
Associate Degrees (2-Year Programs)
An associate degree in computer science or information technology typically covers foundational programming, often Python or Java, introductory data structures, basic database concepts, and networking fundamentals. These programs usually take two years full time at U.S. community colleges and offer a lower-cost entry point for students planning to transfer into four-year computer science or software engineering programs.
Graduates with associate degrees can sometimes secure junior support roles, QA positions, or technical support jobs. However, most employers expect a bachelor’s degree for full software engineering roles. The primary value of associate programs is cost efficiency and transfer opportunities. Many U.S. community colleges have articulation agreements with state universities that allow smooth two-plus-two pathways into bachelor’s programs.
Bachelor’s Degrees (Most Common Path)
A four-year bachelor’s degree, usually a B.S., remains the most common educational requirement for software engineering roles in 2026. This is what most job postings mean when they list a “degree required.”
Common majors include:
Computer Science
Software Engineering
Computer Engineering
Information Technology
Data Science or Applied Mathematics with CS-heavy coursework
Bachelor’s programs typically cover programming in languages such as Java, C++, or Python; data structures and algorithms; operating systems; databases; computer networks; software development life cycle methodologies; and increasingly, introductory artificial intelligence and machine learning by junior or senior year.
Internships during the second or third year are crucial for job readiness. A student graduating in 2027 would ideally complete substantial summer internships in 2026. For founders and CTOs, a bachelor’s degree plus one to two meaningful internships or co-ops remains a strong predictor of readiness for production work.
Master’s & Doctoral Degrees
Master’s degrees, typically one to two years full time, in fields such as computer science, software engineering, machine learning, or data science are increasingly common among senior engineers and AI specialists. Many professionals pursue these degrees after two to five years in industry to specialize, move into research roles, or transition into technical leadership.
AI-focused master’s programs in 2026 commonly include coursework in deep learning, large language models, MLOps, and responsible AI, which is directly relevant for AI and ML engineer roles.
PhDs, often requiring four to six or more additional years, are usually necessary for academic careers and some advanced research roles but are not required for most product engineering positions. Hiring managers should treat graduate degrees as a plus for specialized roles such as applied research, infrastructure, or advanced machine learning, rather than a baseline requirement for general software engineering.
Best Undergraduate Majors for Software Engineers
Not all CS-adjacent degrees are equal. Many modern programs introduced after 2020 integrate project-based learning, capstone projects, and industry collaboration to better prepare students for real-world software work. Below is how common majors align with early-career roles.
Software Engineering Majors
Software engineering degrees focus on the software development life cycle, including requirements gathering, design, implementation, testing, deployment, and maintenance. Curricula emphasize teamwork, Agile or Scrum practices, version control with Git, and real client work in senior capstones.
Graduates are well suited for roles such as application engineer, backend engineer, DevOps-oriented positions, and technical project leads. Many programs now include coursework in secure coding, cloud platforms like AWS, Azure, or GCP, and sometimes introductory machine learning. These majors tend to align closely with how modern product teams operate.
Computer Science Majors
Computer science degrees provide a broad foundation in algorithms, theory, computer systems, and multiple programming paradigms, including object-oriented, functional, and concurrent programming. Common upper-level courses include algorithms, operating systems, databases, distributed systems, compilers, networking, and introductory AI or machine learning.
CS majors are versatile and commonly move into roles such as software engineer, data engineer, machine learning engineer, security engineer, or research engineer. Many AI and infrastructure engineers come from research-oriented CS programs with strong math requirements in linear algebra, probability, and optimization. CS is a good fit for students who enjoy both coding and deeper technical exploration.
Computer Engineering & Related Majors
Computer engineering bridges hardware and software and is often housed within electrical engineering departments. Typical coursework includes digital logic, microprocessors, embedded systems, computer architecture, and low-level programming in C or C++, and sometimes assembly.
Graduates often pursue embedded software, firmware, robotics, IoT, and performance-critical systems roles where hardware knowledge is a clear advantage. These programs frequently include hands-on labs that involve building or programming real hardware, experience that hiring managers should value highly for systems roles. Computer engineering degrees can also lead to general software engineering positions, particularly in systems, performance, and infrastructure work.
Information Technology, Information Systems & Related Business-Tech Majors
Information technology and information systems majors focus more on systems administration, networks, databases, cybersecurity, and business processes than on software engineering theory. Graduates are often well prepared for roles such as system administrator, DevOps engineer, database administrator, security analyst, or internal tools developer.
Motivated IT or IS graduates who build strong coding portfolios can transition into software engineering roles, especially on backend or internal platform teams. For hiring managers, these degrees often signal practical skills in cloud infrastructure, security fundamentals, and enterprise systems. They can be strong backgrounds, though they may require additional depth in algorithms and systems thinking for top-tier software roles.
Data Science, AI & Machine Learning-Oriented Majors
Newer undergraduate majors in data science, AI, and machine learning emerged at many universities. These programs blend statistics, linear algebra, programming, often in Python, and applied machine learning courses covering areas such as deep learning, natural language processing, and computer vision.
Graduates typically pursue roles as machine learning engineer, data scientist, AI engineer, or analytics engineer, especially when paired with solid software engineering skills. Modern curricula often include exposure to large language models, transformers, and MLOps tools such as TensorFlow, PyTorch, MLflow, and Kubernetes. These majors are particularly relevant for founders building AI-first products who need engineers fluent in both software development and machine learning.
Core Skills Employers Expect Beyond the Degree
Degrees are just the starting point. Real hiring decisions in 2026 hinge on skills: building, deploying, and maintaining production-grade software systems. A transcript showing “A” grades in algorithms courses matters far less than demonstrated ability to ship working software under real constraints.
Key skill clusters employers evaluate:
Programming languages and paradigms
Data structures and algorithms
Software design and architecture
DevOps, cloud platforms, and CI/CD
Security fundamentals
Soft skills and collaboration
For AI roles: prompt engineering, model evaluation, LLM integration
Technical Foundations: Programming, Data Structures & Algorithms
Most companies expect proficiency in at least one backend language such as Python, Java, Go, C#, or Node.js, along with familiarity with front-end basics like HTML, CSS, and JavaScript for full-stack roles. Knowledge of multiple languages signals adaptability. A solid understanding of data structures, including arrays, linked lists, trees, hash maps, and graphs, and algorithms such as sorting, searching, dynamic programming, and graph traversal remains critical for both interviews and on-the-job performance. Competitive employers still use algorithmic interviews but increasingly pair them with practical coding exercises and system design discussions. Strong CS fundamentals help engineers adapt faster to new stacks, which is especially valuable on fast-moving AI teams. Common learning resources include university coursework, LeetCode, and HackerRank.
Software Design, Architecture & DevOps Literacy
Mid-level and senior engineers are expected to understand modular design, SOLID principles, design patterns, and core architecture concepts such as microservices versus monoliths, event-driven systems, and API design. Familiarity with cloud platforms like AWS, Azure, or GCP, containers such as Docker, orchestration with Kubernetes, and CI/CD pipelines increasingly differentiates candidates. While some of these skills are introduced in academic programs, many engineers still develop them through hands-on work or targeted online courses.
Strong architectural thinking is particularly important in AI-era systems that handle large data volumes, model inference at scale, and variable load. Founders and CTOs should structure interviews to explore real-world design decisions, including tradeoffs, scaling, and observability, rather than focusing only on language details.
Soft Skills, Collaboration & Product Thinking
With remote and hybrid work now standard, communication, async collaboration, and clear documentation are essential skills. Effective engineers translate user and business needs into technical decisions, prioritize work, and provide realistic estimates.
Top AI and software engineers often stand out through strong product judgment, understanding what to build as well as how to build it. Project-based courses, hackathons, and startup or open-source experience help develop these skills. Hiring managers can evaluate collaboration through pair programming, cross-functional interviews, and behavioral questions that focus on tradeoffs and ambiguity.
Alternative Paths: Bootcamps, Self-Taught, and On-the-Job Learning
Many successful software engineers do not follow a traditional four-year CS degree path. Founders should not overlook this talent pool, as some of the strongest engineers come from non-traditional routes.
Coding bootcamps, typically three to nine months of intensive full-time study, focus on web development, full-stack engineering, or basic data and machine learning skills. Providers such as General Assembly, Flatiron School, and App Academy have produced thousands of working engineers.
Self-taught paths use MOOCs like Coursera, edX, and Udemy, open courseware such as MIT OCW, and project-based learning through GitHub and open-source contributions. Testleaf research suggests it can take six to twelve months of consistent learning with structured projects to become employable as an entry-level software engineer.
On-the-job transitions are increasingly common. Individuals move into software roles from IT, QA, business analysis, or data roles after developing sufficient programming proficiency through side projects or internal opportunities.
While degrees provide structure and credential signaling, companies increasingly rely on skill-based assessment platforms to evaluate candidates from all educational backgrounds fairly and consistently.
Comparing Educational Pathways (Table)
Path | Typical Duration | Approximate Cost (USD) | Strengths | Common Challenges |
Traditional Bachelor’s Degree | 4 years | $40,000–$200,000+ (total) | Deep theoretical foundation, strong credential signaling, structured curriculum, internship pipelines | Slow to update curriculum, high cost and debt, may lack job-ready practical skills |
Coding Bootcamp | 3–9 months | $10,000–$20,000 | Fast path to employment, practical and current skills, portfolio-focused, career services | Limited theory depth, variable quality across providers, may struggle with algorithms |
Self-Taught/Hybrid | 6–18 months (highly variable) | $0–$2,000 (courses, books) | Maximum flexibility, low direct cost, autonomy and self-direction | Harder to signal competence, risk of knowledge gaps, requires strong discipline |
What this means for founders and hiring managers: each path can produce capable engineers when the right conditions are met. The challenge is evaluating candidates consistently, regardless of educational background. This is why skill-based evaluation is important and why platforms like Fonzi, which assess actual engineering capability, provide clearer signals than resume parsing ever could.
How AI Is Transforming Software Engineering Education
Large language models and AI coding assistants such as GitHub Copilot, ChatGPT, and Replit AI have fundamentally changed how students learn programming and how professional engineers work by 2024 to 2026. The implications for education and hiring are significant.
Curricula are adding courses on artificial intelligence, machine learning, data ethics, and human-AI interaction. Many programming assignments now assume students have access to AI tools, shifting the focus from writing code from scratch to using AI effectively, verifying correctness, and understanding what is being built.
This change moves education away from memorizing syntax and toward high-level software architecture, problem decomposition, software testing strategies, and critical review of AI-generated code. Universities and bootcamps are updating academic integrity policies, typically allowing AI tools while requiring students to demonstrate understanding and justify their solutions.
New Curriculum Trends: AI, ML & LLM Literacy
By the 2026 academic year, many CS and SE programs require at least one course in machine learning or AI, with electives covering large language models or natural language processing. Students increasingly build projects using PyTorch, TensorFlow, and hosted model APIs from OpenAI, Anthropic, or open-source models on Hugging Face.
Core topics now embedded in modern curricula include:
Prompt design and prompt engineering
Evaluation metrics such as accuracy, F1, BLEU, and perplexity
Basic MLOps practices including model monitoring, retraining, and versioning
AI ethics, fairness, interpretability, and governance
Retrieval-augmented generation (RAG) architectures
Vector databases and embedding systems
Founders should look for candidates with project experience integrating LLMs into real products. Chatbots, coding copilots, recommendation systems, or RAG applications demonstrate practical AI engineering capability.
AI Coding Assistants as Part of the Learning Process
AI tools now help students generate boilerplate code, tests, and documentation. This shifts educational focus toward reviewing, debugging, and improving AI-generated solutions rather than writing everything from scratch.
Educators in 2026 increasingly design assignments that require explanation of implementation choices, not just final code, to ensure genuine understanding. This mirrors professional practice, and hiring managers should expect engineers to be comfortable pairing with AI tools and using them responsibly.
This evolution makes meta-skills even more important for long-term success, including software design thinking, abstraction, debugging complex systems, and verifying correctness across both computer programs and AI-generated components. Fonzi’s assessments reflect this AI-assisted reality, evaluating how engineers collaborate with AI tools rather than banning them, matching how software projects actually work in 2026.
How Fonzi Helps You Hire Elite AI & Software Engineers, Regardless of Education Path
Fonzi is an AI-native hiring platform that evaluates engineers on realistic, production-relevant tasks. Instead of relying on resumes, school names, or degree labels, Fonzi enables skill-based hiring grounded in actual performance.
Most hires through Fonzi close within approximately three weeks from initial engagement, making it suitable for both fast-moving startups making their first AI hire and larger companies scaling engineering teams.
Fonzi’s approach works equally well for candidates with traditional degrees, bootcamp backgrounds, or self-taught experience. By standardizing skill evaluations around what engineers can actually do, Fonzi reduces bias against non-traditional educational paths.
Fonzi is particularly effective for AI and ML roles, where traditional transcripts may lag behind current frameworks in LLMs, MLOps, and applied AI engineering. University curricula often update slowly; Fonzi’s assessments are updated regularly to reflect current industry practices.
How Fonzi Works
Fonzi uses an AI-driven pipeline to source, screen, and continuously re-assess engineers using project-style challenges, code reviews, and scenario-based evaluations. The process is designed to surface real capability, not test-taking ability.
Candidates complete tasks modeled after production work:
Building a small microservice with specific requirements
Integrating an LLM API into an existing codebase
Improving latency in a data processing pipeline
Debugging a data pipeline or ML system
Designing and justifying software architecture decisions
Fonzi evaluates multiple dimensions: code quality, architectural thinking, software testing discipline, documentation, collaboration signals, and performance under realistic constraints.
Hiring partners receive structured, comparable profiles with evidence of performance. This enables offer decisions based on demonstrated capability rather than degree prestige.
Why Fonzi Is the Most Effective Way to Hire AI Engineers Today
AI engineering evolves rapidly, often outpacing static degree requirements. Most software engineering jobs require skills that formal education alone cannot fully anticipate.
Fonzi continuously updates its assessments to reflect current AI and software stacks, including new LLM frameworks, vector databases, orchestration tools, and deployment patterns. This keeps hiring signals aligned with the actual technology landscape.
Organizations using Fonzi include:
Early-stage startups hiring their first AI engineer
Growth-stage companies scaling engineering teams rapidly
Fortune 500 enterprises building large AI and platform teams
Outcomes:
Faster time-to-hire (often within ~3 weeks)
More consistent evaluation across diverse candidate backgrounds
Reduced bias against non-traditional educational paths
Higher signal-to-noise ratio in candidate pipelines
Think of Fonzi as an extension of your technical hiring team, providing calibrated, battle-tested evaluation processes that would be expensive and slow to build in-house.
Protecting and Elevating the Candidate Experience
Traditional hiring often frustrates candidates with opaque processes, unpaid take-home projects, and delays, especially when recruiters over-index on prestige degrees and screen out capable candidates early.
Fonzi emphasizes clarity and fairness. Candidates understand what they are evaluated on, receive timely updates, and can reuse their performance profiles across multiple opportunities. Challenges are structured to resemble meaningful engineering work rather than abstract puzzles, allowing candidates to demonstrate their real strengths.
This approach leads to more engaged, better-matched candidates who arrive at interviews already familiar with the company’s stack and expectations. For founders and CTOs, this can improve both employer brand among elite engineers and conversion from first contact to signed offer.
Conclusion
While bachelor’s degrees in computer science or software engineering remain common, the real differentiators in 2026 are practical skills, AI literacy, and the ability to design and ship reliable software. Degrees indicate foundational knowledge, not job-readiness.
Alternative paths such as coding bootcamps, self-taught learning, or internal transitions from IT or data roles can produce strong engineers when paired with hands-on experience and solid portfolios.
For founders, CTOs, and hiring managers, the message is clear: treat degrees as one signal among many and focus on evidence-based, skill-focused evaluation. Filtering only by degree or school is increasingly unreliable. Ready to hire elite AI and software engineers based on what they can actually do? Book a call or demo with Fonzi to start hiring within three weeks, regardless of educational background.
