Intern: AI Red Teaming (Fall 2026)
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Before you apply
LocationSunnyvale, CA, Sunnyvale, United States
Typefull-time
SeniorityEntry
DepartmentAI/ML
Company size5+ people
PostedAug 23, 2026 · 2mo ago
Verified live20h ago
At a glanceSummarised by Seekless from the posting.
Prior red-teaming or pen-testing experience like DefCon CTFs or bugbounties
Familiarity with agentic systems and their attack surface — tool calls, retrieval, memory, multi-agent orchestration
Hands-on experience attacking or stress-testing models, from any direction: jailbreaks, prompt injection, adversarial examples, data poisoning, model extraction, or evaluating safety and moderation systems
Able to read a paper and implement its attack
Machine learning tools: pytorch, huggingface, transformers, datasets
Applied deep learning and LLM experience
Training and evaluating deep models
Development environments and tools: unix, git, basic cloud usage on AWS and/or GCP, jupyter
Programming: python
Finetuning LLMs, multi-modal LLMs
Familiarity with ML[NLP,LLM,Vision] interpretability methods, sparse autoencoders, linear probes
Programming languages well-roundedness
Experience in statically-typed and functional languages
Must be authorized to work in the USA or must be able to obtain CPT (Curricular Practical Training) approval from host university
Skills
pytorch
huggingface
transformers
datasets
unix
git
AWS
GCP
jupyter
python
Role Overview
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You will try to break the systems we build and the models we protect, and turn what you find into something the team can act on: a reproducible attack, an evaluation that catches it, and a written account of why it works.
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Expect some mix of: eliciting unsafe behaviour from aligned LLMs and multi-modal models; prompt injection and tool-use abuse against agentic systems; automating attack generation and evaluation rather than hand-crafting one-off prompts; and measuring whether guardrails hold under pressure. Where RealmLabs’ interpretability work gives you access to a model’s internals, use it.
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We aim for a paper or public technical report out of every internship, plus attacks that stay in our evaluation suite after you leave.
Expected Background: Adversarial ML and Red Teaming
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MUST HAVE prior red-teaming or pen-testing experience like DefCon CTFs or bugbounties. General SWE experience is not suitable for this role.
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MUST HAVE Familiarity with agentic systems and their attack surface — tool calls, retrieval, memory, multi-agent orchestration.
•
Hands-on experience attacking or stress-testing models, from any direction: jailbreaks, prompt injection, adversarial examples, data poisoning, model extraction, or evaluating safety and moderation systems.
•
Able to read a paper and implement its attack.
Expected Background: ML
•
Machine learning tools: pytorch, huggingface, transformers, datasets.
•
Applied deep learning and LLM experience.
•
Training and evaluating deep models.
•
(nice to have) finetuning LLMs, multi-modal LLMs.
•
(nice to have) Familiarity with ML[NLP,LLM,Vision] interpretability methods, sparse autoencoders, linear probes — as a way of locating failure modes, not as an end in itself.
Expected Background: Software Engineering
•
Development environments and tools:
•
unix, git, basic clouds usage on AWS and/or GCP
•
jupyter
•
Programming:
•
python
•
(nice to have) “programming languages well-roundedness”
•
experience in statically-typed and functional languages
Compensation & Benefits
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Market aligned compensation for interns in the bay area.
Requirements
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Must be authorized to work in the USA or must be able to obtain CPT (Curricular Practical Training) approval from host university.