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Entry-Level AI Jobs & Internships

Genuinely junior AI roles, classified from what job titles actually say - no "entry level, 3 years required" bait. Every listing links straight to the employer's own application page.

We track 3,984 live AI roles right now - 183 are explicitly entry-level or internships. That scarcity is real, and this is all of them.

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Live entry-level roles & internships

Why are there so few?

AI teams are small and expensive, and most openings are written for people who can own a training run, an eval suite, or a production inference path from day one. The "AI talent shortage" headline counts mid and senior openings - not doors open to beginners.

Most boards make this worse by labeling 2-3-years-experience roles "entry level". We classify by the title the employer actually wrote (junior, graduate, new grad, analyst I, intern), so the number above is small but honest.

The practical consequence: apply fast when a genuine junior role appears, and build the adjacent experience that gets you into the wider pool.

How to actually break in

  • Start adjacent: data engineering, backend or platform engineering, analytics, and annotation or evaluation ops all border AI teams, and all of them hire juniors.
  • Reproduce a paper end to end and write up the gaps you hit. It is the single most legible signal to a research or applied team that you can actually finish things.
  • Build a small eval harness, or a documented retrieval or agent project, and explain the failure modes rather than the demo. A repo somebody can read beats a certificate.
  • Widen your net with remote roles and AI internships.

Entry-level roles by specialization

Entry-Level Research ScienceEntry-Level ML EngineeringEntry-Level AI InfrastructureEntry-Level AI Safety & AlignmentEntry-Level AI Governance & PolicyEntry-Level Evals & Red TeamingEntry-Level Data & MLOpsEntry-Level AI ProductEntry-Level Applied AI

Frequently asked questions

Are there entry-level AI jobs?
Yes, but far fewer than the "talent shortage" headlines suggest. Of the 3,984 live AI roles we track, only 183 are explicitly entry-level or internships right now. They exist - but competition concentrates on them, so treat every listing here as time-sensitive.
Why are entry-level AI jobs so hard to find?
AI teams are small and expensive, and most openings are written for people who can own a training run, an eval suite, or a production inference path from day one. Many "entry-level" postings elsewhere quietly require 2-3 years of experience; we classify by what the job title actually says, which is why our entry-level count is honest - and small.
Do AI internships count as entry-level?
Yes - we include internships on this page. For most people an internship or an adjacent role (data engineering, backend engineering, analytics, or annotation and evaluation ops) is the realistic first step, because it produces the hands-on evidence AI hiring managers screen for.
What do you need to get an entry-level AI job?
Evidence that you can do the work, in public. The three that consistently move screens: a paper you reproduced end to end (with the gaps you hit written up), a small eval harness you built and the results it produced, and a documented retrieval or agent project where you explain the failure modes rather than the demo. A degree helps for research roles; for engineering roles a repo somebody can read beats a certificate.
How do I get an AI job with no experience?
The proven paths are sideways moves: data engineering into MLOps, backend or platform engineering into AI infrastructure, analytics into applied ML, and annotation or evaluation ops into evals and red teaming. Pair one adjacent role with public evidence - a reproduced paper, an eval harness, a documented retrieval project - then apply to explicitly junior postings like the ones listed here.

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