The Best Entry-Level Tech Jobs Safe from AI

You know how everyone says the tech industry is changing fast? Well, AI is only increasing that pace. And not in the ways you might totally expect, either. See, AI is automating routine tasks, which do indeed make up a lot of the work of entry-level work, but that just means that entry-level jobs are going to look different in 2026 and beyond. Jobs that need complex problem-solving, human judgment, client interaction, and hands-on technical work? They’re here to stay, they need people like you, and you can get one of them.

So dive in. This guide covers eight of the most AI-resistant entry-level tech careers in the United States, including three emerging AI industry roles that pay the highest entry-level salaries in tech. Here’s why they're automation-proof, what you can earn, and how to actually break into these fields in months instead of years.

The real challenge to launch tech careers for beginners: Entry-level positions are under pressure

We know, if you're thinking about a tech career, you've probably heard mixed messages. Headlines say AI will kill millions of jobs, but major companies are simultaneously desperate for talent. Here's what's actually happening, we’re not going to hide it: entry-level positions have become the main target of automation, not senior roles.

Junior-level tech job postings have dropped 34% from earlier peaks, according to U.S. Bureau of Labor Statistics data. Experience requirements keep getting higher too. Entry-level roles that wanted two years of experience now ask for three to five.

But that ignores the simultaneous industry developments that are working in the opposite direction. Roles requiring hands-on technical presence, real-time decisions, and human interaction are exploding. In addition, the AI industry is creating completely new job categories that need human expertise in training, evaluating, and refining AI systems. Guess what: all throughout these new sectors, you can find entry-level roles that are eager to hire people fresh to the industry.

The 8 best entry-level tech jobs that AI can't replace

1. Prompt engineer

Prompt engineers design, test, and optimize prompts for large language models like ChatGPT and Claude. They write sophisticated instructions that get AI systems to produce high-quality outputs for specific business uses. The role mixes psychology, writing, technical understanding, and strategic thinking.

Entry-level prompt engineers work on optimizing prompts for customer service chatbots, content generation systems, data extraction tools, and decision-support systems.

Why it's AI-resistant

While this role expressly involves working with AI, the work is fundamentally human:

1. It requires a human understanding of nuance: AI models can't evaluate their own outputs in terms of what humans are looking for.
2. It asks for domain expertise: To keep outputs in line and prevent hallucination, a human with an understanding of their field (legal, medical, technical) needs to be in the loop.
3. It needs human judgment: Strategic thinking about system behavior and edge cases is a crucial aspect, something that humans do easily but AI struggles with.

Salary and growth outlook

This is the highest-paying entry-level AI role in 2026:

• Entry-level (0–1 year): $95,000–$130,000 (median $109,000–$126,000)
• Mid-level (3–5 years): $140,000–$175,000
• Senior (6+ years): $205,000–$270,000
• Top specialists and architects: $300,000+

The current job growth is explosive, as prompt engineering is one of the most in-demand skills in 2026, with a severe talent shortage and every major company hiring for it.

Career path

Junior prompt engineer → prompt engineer → senior prompt engineer → prompt architecture lead → AI product strategy lead.

How to land the job

• Join an AI automation bootcamp designed to launch your prompt engineering career
  
  • Become an expert with ChatGPT, Claude, and other LLMs
  • Learn fundamental AI/ML concepts (no coding required initially)
  • Take structured courses on prompt engineering and generative AI
  • Build a portfolio of real prompt engineering projects with documented results
• Learn basic Python and API calls (valuable but not required initially)
• Network with AI/ML teams on LinkedIn and Discord communities

2. AI data annotator / AI trainer

AI data annotators and trainers label data, evaluate AI outputs, refine training datasets, and help AI systems learn to behave the way humans want them to. The role includes data labeling, quality assurance on model outputs, providing human feedback that improves AI behavior, and applying specialized domain expertise. Entry-level positions focus on basic annotation and quality checks.

And people with these skills are in demand. Companies from Microsoft to Google to Anthropic employ hundreds of AI trainers, as this work is crucial to their operations; the quality of data and training directly impacts their model quality.

Why it's AI-resistant

This role is fascinating because it directly involves training AI, yet it's remarkably resistant to automation:

1. It takes judgment and domain expertise: Annotating medical data requires understanding medicine. Evaluating legal AI outputs requires legal knowledge. This judgment can't be automated.
2. It requires creative quality control: Identifying when an AI system is biased, hallucinating, or making logical errors requires human intuition. Finding edge cases and failure modes is creative detective work.
3. It asks for ethical responsibility: AI trainers are responsible for ensuring AI systems are safe, fair, and compliant. This human oversight is non-negotiable.

Salary and growth outlook

• Basic data annotation roles: $40,000–$50,000 (entry point for non-technical backgrounds)
• AI trainer/advanced annotation: $60,000–$85,000 (technical roles with more responsibility)
• AI training specialist (3–5 years): $55,000–$80,000
• Senior AI operations manager/quality lead: $100,000–$150,000+

The data annotation market is projected to grow 24% annually, reaching $1.1 billion globally by 2032. Salaries are rising fast due to severe talent shortage and increasing task complexity.

Career path

AI data annotator → AI training specialist → AI quality assurance lead → AI operations manager → senior AI training manager.

How to land the job

• Start by gaining the necessary skills in a get-hired AI automation bootcamp
• Develop core skills:
  
  • Attention to detail and accuracy (essential)
  • Domain expertise in any field (medicine, law, engineering)
  • Clear written communication
  • Basic data tools (Excel, Google Sheets)
  • Understanding of AI limitations and failure modes
• Explore platforms that offer data annotation gigs:
  
  • Mindrift.ai: Global AI training platform (10,000+ AI trainers)
  • Appen: Data annotation platform with thousands of projects
  • Scale AI: Specializes in AI training and evaluation
  • OpenAI Collaborative Filtering: Apply to OpenAI's contractor program
• Build a portfolio showing annotation quality and consistency
• Take online AI safety/alignment courses to augment your know-how

3. Machine learning engineer

Machine learning engineers build and deploy AI systems. They write code that trains machine learning models, integrates models into products, and monitors model performance. The role bridges software engineering and data science, requiring strong coding skills combined with mathematical understanding.

Entry-level ML engineers work on building data pipelines, training models using TensorFlow and PyTorch, evaluating model performance, deploying models to production, and collaborating with data scientists and product managers.

Why it's AI-resistant

Machine learning engineering involves applying strategic system design, architectural decisions, and creative problem-solving, all of which are based on human intuition and knowledge:

1. System architecture: Designing data flow through models, training strategies, and deployment requires judgment and creativity, not routine coding.
2. Optimization and trade-offs: Improving model accuracy while reducing latency and costs involves navigating complex trade-offs that AI struggles with. No two optimization problems are identical.
3. Model debugging: When models perform poorly, the cause could be data quality, architecture, hyperparameters, or deployment issues. Diagnosing root causes requires human intuition.
4. Real-world constraints: Deploying models requires handling latency, costs, fairness concerns, and regulatory compliance. These strategic decisions are human work.

Salary and growth outlook

• Average entry-level: $69,362–$98,945 annually
• Range: $51,500 (25th percentile) to $78,500 (75th percentile), top earners $102,500+
• Mid-level (5–10 years): $112,000–$180,000
• Senior roles (10+ years): $150,000–$350,000+

ML engineers are in extreme demand. Major tech companies (Meta, Google, Microsoft, Amazon) report that ML engineer recruiting is their top hiring priority.

Career path

Junior ML engineer → ML engineer → senior ML engineer → ML team lead → ML staff engineer → director of ML / ML architect.

How to land the job

• Gain a strong foundation (it’s easiest to go with a project-based AI & ML bootcamp to gain these skills): 
  
  • Python: Data structures, algorithms, and libraries (NumPy, Pandas)
  • Mathematics for ML: Linear algebra, calculus, probability, statistics
  • ML frameworks: TensorFlow, PyTorch, scikit-learn
  • Data pipeline tools: SQL, Apache Spark, data orchestration platforms
• Build real ML projects: End-to-end projects from data collection to deployment
• Contribute to open-source projects: TensorFlow, PyTorch, scikit-learn projects
• Build experience (Hint: a good bootcamp will give you a good portfolio and set you on your way with these last three points as well)

4. Cyber security analyst

Cyber security analysts monitor systems for security threats, analyze vulnerabilities, respond to incidents, and implement security measures. Entry-level cyber security analysts typically support larger security teams as SOC (Security Operations Center) analysts, conducting threat investigations and assisting with compliance documentation.

Why it's AI-resistant

Cyber security requires judgment calls demanding human creativity and strategic thinking about:

1. Threat severity: How should resources be allocated? How urgent is it to tackle a potential threat? AI can’t answer questions like these, making human judgment crucial.
2. Responses to novel attacks: Attackers keep innovating, so the workers keeping these bad actors at bay need to likewise innovate. This creative thinking is something humans excel at, and AI doesn’t.
3. Organizational risk tolerance: There’s no one overarching model for risk tolerance; it differs industry-by industry and company-by-company. That requires a strong sense of ethics and ability to quickly adapt — something humans are good at, but AI isn’t.

Salary and growth outlook

• $62,000–$75,000 entry-level, with 32% job growth through 2032
• Mid-level (3–5 years): $85,000–$115,000
• Senior professionals (6–10 years) earn $120,000–$165,000

Career path

Cyber security analyst → senior analyst → security engineer → security architect → CISO ($200,000+).

How to land the job

• Gain the skills and bona-fides to launch your career in a reputable cyber security bootcamp 
• Pursue a CompTIA Security+ certification (a good bootcamp will often end with you graduating with this certificate in-hand)
• Build a home lab for threat detection practice
• Study SOC analyst job descriptions to understand required tools
• Network in cyber security communities

5. Web developer

Junior web developers design, build, and maintain websites and web applications. They write front-end code (HTML, CSS, JavaScript, React), back-end logic (Node.js, Python, Java), and coordinate with designers and product teams. Unlike routine coding, development requires architectural thinking, creative problem-solving, and close collaboration with stakeholders. Check out Nathanael Anderson's story to learn more.

Why it's AI-resistant

AI coding assistants can help you code, but crucial parts of the job are beyond the capabilities of AI:

1. Creative problem-solving: Balancing business and user needs along with fresh, eye-catching design — this is something only a human can do.
2. Architectural decision-making: AI can’t make trade-offs among frameworks, database structures, or authentication patterns, for example. The fit for the team and long-term implications need to be handled by a human.
3. Comprehensive debugging: The sheer amount of things that can go wrong with code is immense: issues between only occasionally misaligning functional components, in only very specific environments, or with third-party code etc., etc. To fix errors among all this requires a comprehensive understanding of the tech as well as human intuition for the error's cause, something that AI can’t replicate.

Salary and growth outlook

• $94,149 average entry-level, ranging $63,000–$101,500
• Top earners reach $153,000+
• Job growth for software developers is projected at 10.4% annually, representing one of the fastest-growing occupational categories

Career path

Junior developer → mid-level developer → senior developer → tech lead → engineering manager or architect.

How to land the job

• Gain requisite know-how and bona-fides in an employment-focused bootcamp
  
  • Learn a modern tech stack (JavaScript/React, Node.js/Express)
• Build 3–5 real projects on GitHub
• Complete coding challenges on LeetCode
• Contribute to open-source projects

6. IT support specialist / help desk technician

IT support specialists troubleshoot hardware and software issues, set up user devices, manage system access, and provide technical assistance via phone, email, or chat. While indeed entry-level, this role is the practical foundation for understanding organizational IT infrastructure that can then fuel major career growth. This is an ideal first step for building knowledge before specializing in network administration, systems administration, QA, or cyber security. In fact, this growth is exactly what Pedro Fierros accomplished

Why it's AI-resistant

We’re sure you’ve tried to solve some infuriating technical issue through a company’s “New And Efficient Support Chatbot!” and kept trying to talk to an actual human. Yeah, real customer support requires:

1. Clear communication: Interpreting issue explanations from customers and clients at all levels of technical fluency and then replying with solutions in a manner they can understand is vital. AI struggles to both match vague issue descriptions to potential solutions and to cater its language on a case-by-case basis.
2. The ability to diagnose unique problems: When something completely unexpected happens, human intuition becomes crucial. AI can only suggest solutions it has already seen multiple times, so when something new comes up, human investigation is necessary.
3. Patient, persistent, creative thinking: When a tech issue arises, the causes can be complex and opaque, requiring lateral thinking, persistence, and intuition, qualities that AI lacks but that humans have in abundance.

Salary and growth outlook

• Entry-level IT support specialists earn $62,000 per year
• Mid-level positions (1–3 years) earn $66,000
• Senior roles (4–6 years) get $70,000+
• IT support specialist positions are projected to grow 6% through 2033, adding approximately 62,700 job openings annually

Career path

IT support specialist → IT support technician → network administrator → systems administrator → IT manager.

How to land the job

• Gain an understanding of the workings of software by joining an employment-focused tech bootcamp
  
  • Learn basic networking concepts
  • Develop soft skills in communication and problem-solving
• Earn a CompTIA A+ certification
• Gain hands-on OS experience (Windows, macOS)

7. Quality assurance (QA) engineer

QA engineers test software to identify bugs, verify that features work as intended, and ensure overall product quality. Entry-level QA roles typically begin with manual testing but quickly progress to test automation using tools like Selenium or Cypress. Find out more by reading Ryley Johnson's story.

Why it's AI-resistant

While AI can identify some bugs, comprehensive testing requires human intuition and creativity. QA engineers must:

1. Understand business requirements: What need is the test fulfilling? How deeply or broadly should the tests be carried out? These are judgment calls that AI can’t make.
2. Anticipate user behavior: What unintended ways will a person behave when using the software? AI can’t intuit the ways in which someone may misuse the program. But with human empathy and experience, a person can. 
3. Design atypical test scenarios: What odd edge cases will need to be tested? A human can design tests even beyond what would be considered usual usage, but AI, being a prediction machine, can’t imagine beyond typical patterns.

Salary and growth outlook

• Entry-level QA engineers earn $62,480 annually
• There is 5% projected job growth (faster than average through 2034)

Career path

QA analyst → senior QA engineer → QA lead → QA manager or test automation architect.

How to land the job

• Gain the necessary knowledge and skills in a bootcamp staffed by industry-seasoned experts
  
  • Learn manual testing methodologies
  • Study test automation tools (Selenium, Cypress)
  • Practice writing clear, reproducible bug reports
  • Get a handle on basic SQL
• Join online QA communities and network

8. Network operations center (NOC) technician

NOC technicians monitor a company's network infrastructure around the clock, identify outages or performance issues, and execute remediation steps. They work with routers, switches, firewalls, and network monitoring tools.

Why it's AI-resistant

Network problems show up unpredictably. While monitoring software can absolutely flag anomalies, technicians must: 

1. Diagnose root causes: A problem’s origins are, ideally, not already known. That means that rooting out the cause takes creative investigation that AI can’t carry out and that will need a human in the loop.
2. Coordinate teams: A knowledge of which team members are best at which tasks and assigning them appropriately is something that takes a human relationship to properly carry out, and this is beyond the capabilities of AI.
3. Make real-time network change decisions: When novel, untrained-for, and time-sensitive decisions need to be made, humans have the plasticity and adaptability to act, but AI doesn’t.

Salary and growth outlook

• Entry-level NOC technicians earn $60,500 annually
• NOC roles are relatively stable with modest growth but excellent stepping stones into network administration and systems engineering

Career path

‍NOC technician → network administrator → senior network engineer → network architect.

How to land the job

• Start by gaining a comprehensive understanding of networks and security by joining a credible cyber security bootcamp
• Earn a CompTIA Network+ certification
• Learn TCP/IP, DNS, DHCP, VPNs, firewall basics
• Gain experience with monitoring tools (Nagios, Zabbix, SolarWinds)
• Study Cisco CCNA (optional but valuable)

Why go with TripleTen to land AI-proof tech jobs

TripleTen offers numerous get-hired programs that are explicitly focused on getting graduates tech careers that will stick — yes, even in the age of AI. Not only that, but the experts behind the syllabi stay up to date with what’s going on in tech, meaning the programs keep developing in response to changing conditions. In addition, TripleTen is training its students to be major players in AI through its AI Automation and AI & Machine Learning programs. Want to be ready for the future of tech? TripleTen is a reliable ally in your journey.

What makes TripleTen different

Career Acceleration program

• Personalized career coaching from experts starting from early in the program
• Resume and LinkedIn profile reviews
• Mock interview practice — including technical interviews
• Job search strategy development

Learning support

• Access to experts to answer any thorny questions you may have
• Integrated community of learners who you can check in with for help or a chat
• Group tutoring sessions
• AI-powered learning assistant (Dot)
• Code reviews on every project
• Flexible sprint-based schedule

Alumni track record

• 1,100+ graduates
• 82% employment rate
• Alumni hired by Spotify, Microsoft, Google, and Booz Allen Hamilton
• Average time to first job: 4–6 months after graduation

Money-back guarantee

If you follow the guidance of TripleTen's career experts and don't land a tech job within 10 months of graduation, we'll refund your entire tuition. More info.

FAQ

Can I get an entry-level tech job without experience or a degree?

Yes, you can absolutely get an entry-level tech job without experience or a degree. Recruiters are more interested in skills (which you can gain at a bootcamp) and projects proving your bona-fides (which you’ll also gain at TripleTen). For some roles, you’ll also need certification (e.g., CompTIA Security+ for cyber security). But just prove your know-how, and you can land an entry-level role.

How long until I'm job-ready after a bootcamp?

You’re job-ready immediately after you finish a bootcamp, and TripleTen programs last from three to nine months depending on subject and whether you study full- or part-time. After that, expect the job search to take some time. For some data: TripleTen’s money-back guarantee lasts for 10 months post-graduation, after which you can request a refund if you haven’t landed a job.

What if I can't afford bootcamp costs?

It’s likely that TripleTen’s experts can help you find a financing option that will help you cover the cost of the bootcamp. In fact, one of our programs costs less per month than the average American spends on entertainment. If no options work for you, consider getting a head start by exploring tech with free resources as you save for the cost of tuition.

Is prompt engineering really accessible without coding?

Prompt engineering, by design, is accessible without coding. It requires zero coding for entry-level roles, and you can even get started now by checking out free resources on Medium or YouTube. To really turn this skill into a career, consider enrolling in a get-hired AI Automation program.

What if I'm not good at math—can I still do ML engineering?

You can get into ML engineering without being a math genius. To be fair, in some roles, you will need a basic familiarity with statistics and linear algebra, which you can learn in a good AI & ML program. However, applied ML roles emphasize coding and practical problem-solving instead of mathematical prowess.

Should I do a bootcamp while working full-time?

You can do a bootcamp while working full-time; TripleTen’s part-time programs were all designed to allow you to study for a better career while keeping your job. But if you want to study full-time to get into tech faster, it’ll be difficult to work simultaneously. Ultimately, the choice of speeding to a tech career or maintaining income is up to you.

Which entry-level role pays best and which are easiest to start?

The entry-level roles that pay best are prompt engineer, web developer, and ML engineer, while the ones easiest to begin with are IT support, manual QA, and data annotation. The roles easier to start do begin with lower salaries, but often serve as vital first steps in paths toward lucrative careers.

Do I really need a portfolio to get hired?

Yes, a portfolio is crucial to get hired in tech. If you’re starting from scratch, a bootcamp like TripleTen will help you build out a portfolio by design with its externships and project-based approach to education. To build out a portfolio otherwise, consider getting active on GitHub, open source work, or tinkering on a personal project.

What's the biggest mistake career changers make?

The biggest mistake career changers make is not locking in fundamentals of the field they’re switching into. More complex topics are fun, but the fundamentals are what will serve you well, and they’re the basis of the later more advanced skills. The second major mistake is focusing on skills instead of projects — recruiters want to see applied know-how.

‍