The best entry-level AI jobs, in our view, are junior AI engineer, junior ML engineer, data analyst on an AI team, AI trainer or evaluator, AI operations associate, solutions associate, AI sales development rep, AI customer support, research assistant and trust and safety associate. Several need no coding; the technical ones reward shipped projects more than credentials.
Definition: An entry-level AI job is a role on an AI product, model or team that hires people with roughly 0 to 1 years of experience. Most are not research roles, and many sit in sales, support and operations at AI companies.
That second sentence is where most beginners go wrong. They aim at the one door with the longest line.
What the market looks like for beginners
Demand for AI skills is clearly rising. The Stanford AI Index 2026, using Lightcast data, puts the share of US job postings requiring AI skills at about 2.6 percent in 2025, up from the year before, with Python the most requested specialized skill.
The entry level is a different story. The August 2026 update of the "Canaries in the Coal Mine" paper by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen, using ADP payroll data through June 2026, finds employment of 22 to 25 year olds in the most AI-exposed occupations stands 19 percent below where it would be had it kept pace with less-exposed peers. The declines concentrate in jobs where AI automates tasks, and not in jobs where it complements people.
Our read: the entry-level AI jobs worth chasing are the ones where you work with AI rather than compete with it. That's the lens behind this list.
Entry-level AI jobs at a glance
| Role | Coding needed? | Public pay benchmark | Typical first proof |
|---|---|---|---|
| Junior AI engineer | Yes | $135,980 BLS median, software developers | A shipped app on a model API |
| Junior ML engineer | Yes | $120,230 BLS median, data scientists | A trained, evaluated model |
| Data analyst on an AI team | SQL, some Python | No direct BLS category | Dashboards and analysis memos |
| AI trainer or evaluator | Usually no | $25 to $50 an hour for general work (DataAnnotation's own range) | Careful writing, domain expertise |
| AI operations associate | Light | No direct BLS category | Process and tooling work |
| Solutions associate | Some | No direct BLS category | Demos built for real use cases |
| AI sales development rep | No | No direct BLS category | Results from target-driven jobs |
| AI customer support | Light | No direct BLS category | Clear writing, product fluency |
| Research assistant | Yes | Varies by lab or program | Coursework, replications |
| Trust and safety associate | No | No direct BLS category | Policy writing, judgment |
A note on the numbers. BLS medians cover all experience levels, so starting pay usually sits below them, and the BLS doesn't track titles like AI engineer separately. For a new-grad anchor, NACE's Winter 2026 Salary Survey projected an average starting salary of $81,535 for computer sciences graduates (base pay, 150 employers). The 1752vc salary guide is another reference for startup and AI roles.
The 10 best entry-level AI jobs
1. Junior AI engineer
What it is: building product features on top of existing models: retrieval, agents, prompts, evals and the plumbing around them. In our view this is one of the faster-growing technical doors for new grads, because it rewards building over theory.
What it needs: solid software skills (Python or TypeScript), API work, and evidence you can measure whether an AI feature works.
Pay data: the BLS has no AI engineer category. Software developers had a median of $135,980 in May 2025, which is the closest public benchmark.
2. Junior ML engineer
What it is: training, fine-tuning, deploying and monitoring models, closer to the data and the math than an AI engineer.
What it needs: Python, statistics, an ML framework and some data engineering. Many postings ask for a related degree; some prefer a master's.
Pay data: the BLS's nearest category, data scientists, had a median of $120,230 and 35 percent projected growth for 2025 to 2035.
3. Data analyst on an AI team
What it is: measuring how AI features perform with users: usage, quality metrics, costs and experiments.
What it needs: SQL, a BI tool, basic statistics and clear written summaries. It's one of the more realistic AI-adjacent first jobs for math, economics and business majors.
4. AI trainer, annotator or evaluator
What it is: writing and grading model responses, labelling data, and building test cases that check model quality. With experience, people move up to annotation lead, quality reviewer or evals specialist.
What it needs: careful reasoning, strong writing and, for better-paid work, subject expertise (coding, law, medicine, math, languages).
Pay data: DataAnnotation, one company in this market, lists $25 to $50 an hour for general projects and higher rates for coding and specialist work, as flexible contract work. We'd treat this as a strong way to learn how models fail, and a weaker form of job security: it is usually contract work, and hours can vary.
5. AI operations associate
What it is: keeping AI workflows running: managing data pipelines and vendors, triaging model issues, documenting processes. Titles vary (AI ops, ML ops associate, model operations).
What it needs: organization, spreadsheet and scripting comfort, and the ability to learn internal tools fast.
6. Solutions associate
What it is: helping customers adopt an AI product: scoping use cases, building demos, answering technical questions alongside account executives. It's the junior cousin of the solutions engineer and forward-deployed engineer roles. Joe Schmidt of a16z called the forward deployed engineer the hottest job in startups in a 2025 essay, arguing that AI products for complex enterprise workflows win on hands-on implementation, which is why model labs hire for it too.
What it needs: enough technical depth to build a working demo, plus comfort talking to customers. Engineers who like people tend to do well here.
7. AI sales development rep
What it is: researching prospects, writing outreach and booking meetings for an AI company's sales team.
What it needs: resilience, good writing and genuine fluency with the product. In our view, selling AI well often means explaining what it can't do.
8. AI customer support or support engineer
What it is: helping users with an AI product, diagnosing bugs and turning patterns in tickets into product feedback.
What it needs: patience, clear writing and product fluency; support engineer versions add debugging and API skills. One caution: the Canaries paper uses customer service as one of its case studies of AI-exposed work, so we'd favor support roles that lean technical or relationship-heavy over high-volume ticket work.
9. Research assistant
What it is: helping a lab or research team run experiments, clean data, replicate papers and write code. For students, this usually happens on campus.
What it needs: strong coursework in math and programming, and ideally a replication of a known result. Research scientist roles themselves typically require a master's or more; the BLS lists a master's degree as the typical entry point for computer and information research scientists (median $140,300).
Other routes: some labs run paid programs for early researchers. OpenAI's Residency, for one, is a six-month research program whose page invites people from quantitative and scientific fields, including self-taught and non-traditional applicants. Check each lab's own page for whether it is open.
10. Trust and safety associate
What it is: reviewing policy-violating content and model behavior, writing usage policies, and testing models for misuse.
What it needs: judgment, clear writing and calm under messy cases. Law, policy, psychology and journalism backgrounds map well. Our guide to non-technical AI jobs covers this family in more depth.
Best AI jobs for college students
College is the cheapest time to get AI experience. Some routes we'd consider:
- Research Experiences for Undergraduates (REU). The National Science Foundation funds REU sites where undergraduates join research projects, including computer science sites. Students apply to each site directly, and participants need to be US citizens, nationals or permanent residents.
- Your own university's labs. Email two or three professors whose papers you've read, with one specific question and a small replication attached. A research assistant role often starts that way.
- Part-time AI training work. Evaluation and annotation projects can fit around classes and teach you how models fail.
- Internships at AI startups. They tend to hire later and less formally than big companies. Our guide on how to get an AI internship covers timelines.
- Build in public. A small app on a model API, with an eval set and a write-up, is the kind of work Anthropic's careers page invites candidates to put at the top of a resume (it lists independent research, blog posts and open source).
Best AI jobs for recent graduates
Recent grads often compete with experienced candidates, so our suggestion is to pick doors where a new grad is a natural fit:
- CS or engineering grads: junior AI engineer, junior ML engineer, solutions associate.
- Math, statistics or economics grads: data analyst on an AI team, evals specialist.
- Business grads: AI sales development, solutions associate, AI operations.
- Humanities, law-track or social science grads: trust and safety, AI training and evaluation, customer support.
If the AI track feels too narrow, our list of the best entry-level tech jobs covers the wider market.
How to land an entry-level AI job: a six-week sprint
An illustrative plan for a student or new grad with about 10 hours a week:
- Week 1: pick one role from the list. Read 25 entry and internship postings for it on the 1752vc AI jobs track, filtered by level, and list the skills that repeat.
- Weeks 2 to 4: build one piece of proof for that role. For technical roles, an app plus a 50 to 100 case eval set; for non-technical roles, a written teardown of an AI product's onboarding or a sample policy.
- Week 5: rewrite your resume so the proof sits at the top, with links.
- Week 6 onward: apply to about 6 fresh postings a week (filter by Past 7 days) and send a short note to someone on each team.
Run the numbers: 6 applications a week for 6 weeks is 36. If 1 in 10 becomes a conversation (an illustrative rate, not a benchmark), that's three or four interviews, each with something concrete to discuss. For the bigger picture, our pillar on how to get a job in AI has a 90-day plan, and if you are starting from zero, see how to get an AI job with no experience.
"AI is eating the entry-level jobs"
This deserves a straight answer. The Canaries data shows real declines for young workers in AI-exposed jobs, and software development and customer service are among the examples the authors discuss.
But.
The same paper finds no similar decline where AI complements workers rather than automating them. Usage data points the same way: Anthropic's Economic Index report from September 2025 found that 77 percent of business uses of Claude through its API followed automation patterns, against roughly half on the consumer app, so the tasks companies hand off whole are the ones to steer away from. Many roles on this list (solutions, evals, trust and safety, AI engineering) exist because of AI. Our view: the risk sits in generic, repeatable junior work, and the opportunity sits in being the person who makes AI work for a team.
Common mistakes
- Applying only to research roles. They're the narrowest door, and most ask for graduate training.
- Listing tools instead of results. "Familiar with LLMs" says little. A linked app with an eval score says a lot.
- Ignoring the business side. Sales, solutions and operations roles at AI companies often hire juniors faster than engineering does.
- Treating annotation as a dead end. Done well, it can lead to quality, evals and operations roles.
Where we land
If we had to choose for most beginners: junior AI engineer for strong coders, evals or data analyst work for quantitative people, and solutions or sales development for people who like customers. Each puts you next to AI work that is growing rather than shrinking.
That's our pick list, not a verdict. Your skills and what you enjoy should decide the door. Browsing the AI jobs track by Entry level is a quick way to see which of these roles is hiring near you.
The bottom line
Most AI jobs are not research jobs.
The first one is usually the job next to the model, not inside it.
Key takeaways
- Strong entry-level AI jobs include junior AI or ML engineer, data analyst, AI trainer or evaluator, AI operations, solutions associate, AI sales development, support, research assistant and trust and safety.
- AI skill demand is rising (about 2.6 percent of US postings in 2025, per the Stanford AI Index using Lightcast data), but young workers in automatable AI-exposed jobs have lost ground, per the Canaries paper.
- Public pay data is thin for AI titles; BLS medians for software developers and data scientists are the closest benchmarks and include experienced workers.
- College students can get AI experience through REU sites, campus labs, part-time evaluation work and AI startup internships.
- Pick one role, build one piece of proof that looks like the job, and apply early to fresh postings.
Frequently asked questions
AI training and evaluation work (writing, rating and labelling model outputs) is usually the most open to beginners, since many projects need careful reasoning rather than coding. AI customer support and sales development roles are also open to people without technical degrees. They teach how AI products work and can lead to operations, quality or solutions roles.
Students can work as research assistants in campus labs, join NSF-funded REU research sites, take part-time AI training and evaluation projects, or intern at AI startups. A small public project, such as an app built on a model API with a short evaluation write-up, makes any of these applications stronger and gives interviewers something concrete to discuss.
Public data is limited because the BLS doesn't track AI titles separately. Its closest benchmarks are a May 2025 median of $135,980 for software developers and $120,230 for data scientists, both including experienced workers. NACE projected $81,535 average starting pay for computer sciences graduates in 2026. Contract AI training work is often paid hourly.
Most do not. Research scientist roles typically do, and the BLS lists a master's degree as the typical entry point for computer and information research scientists. AI engineering, data analysis, solutions, operations, sales, support and evaluation roles usually hire on a bachelor's degree or demonstrated skill, and some research programs say they welcome self-taught and non-traditional applicants.
They can be. Annotation and evaluation work shows you how models behave and fail, which is useful for evals, quality and operations roles later. The trade-off is stability: much of it is flexible contract work with variable hours. We'd treat it as paid learning and a resume line, alongside a search for a full-time role.
Sources
- Stanford HAI: AI Index Report 2026, Chapter 4: Economy
- Stanford Digital Economy Lab: Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence (August 2026 update)
- US Bureau of Labor Statistics: Software Developers, Quality Assurance Analysts, and Testers
- US Bureau of Labor Statistics: Data Scientists
- US Bureau of Labor Statistics: Computer and Information Research Scientists
- NACE: Class of 2026 Salary Projections Are Promising
- National Science Foundation: Research Experiences for Undergraduates (REU)
- DataAnnotation: AI training work
- OpenAI: Residency
- Anthropic: Careers
- a16z: Trading Margin for Moat: Why the Forward Deployed Engineer Is the Hottest Job in Startups
- Anthropic: Economic Index Report, Uneven AI Adoption (September 2025)
Disclaimer: This guide is for general education only and is not legal, tax or investment advice. Laws, market data and program terms change, so it may not reflect the latest developments or fit your situation. Treat it as a starting point, not a source of truth, and talk to a qualified lawyer, accountant or financial adviser before you make decisions.
