Intern: AI Red Teaming (Fall 2026)
Realm Labs
Intern: AI Red Teaming (Fall 2026) Sunnyvale, CA
AI/ML
In office Full-time Role Overview 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. 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. 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 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.- nice to have) Offensive security background outside ML: CTFs, vulnerability research, penetration testing.
- nice to have) Familiarity with agentic systems and their attack surface - tool calls, retrieval, memory, multi-agent orchestration.
- 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.
Vacancy posted 2 days ago
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