{"id":1270712,"url":"https://alion.io/job/realm-labs-intern-ai-red-teaming-fall-2026","title":"Intern: AI Red Teaming (Fall 2026)","company":{"id":2203168,"name":"Realm Labs","domain":"realmlabs.ai","url":"https://alion.io/company/realmlabs","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Gem","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Security","role_family":"Security","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":49000,"max_usd":70000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":9},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Hugging Face","optional":false},{"name":"Interpretability","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"PyTorch","optional":false},{"name":"Red Teaming","optional":false},{"name":"AWS","optional":true},{"name":"Fine-tuning","optional":true},{"name":"GCP","optional":true},{"name":"NLP","optional":true},{"name":"Python","optional":true},{"name":"Unix","optional":true}],"status":"live","first_seen_at":"2026-08-19T00:56:05Z","employer_posted_date":"2026-08-24","last_verified_at":"2026-11-01T04:09:07Z","board_verified":true,"closed_at":null,"days_open":74,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":74},"description":"Role Overview\nYou 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.\nExpect 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.\nWe aim for a paper or public technical report out of every internship, plus attacks that stay in our evaluation suite after you leave.\nExpected Background:Adversarial ML and Red Teaming\nMUST HAVE prior red-teaming or pen-testing experience like DefCon CTFs or bugbounties. General SWE experience is not suitable for this role.\nMUST HAVE Familiarity with agentic systems and their attack surface - tool calls, retrieval, memory, multi-agent orchestration.\nHands-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.\nAble to read a paper and implement its attack.\nExpected Background: ML\nMachine learning tools: pytorch, huggingface, transformers, datasets.\nApplied deep learning and LLM experience.Training and evaluating deep models.\n(nice to have) finetuning LLMs, multi-modal LLMs.\n\n(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.\nExpected Background: Software Engineering\nDevelopment environments and tools:unix, git, basic clouds usage on AWS and/or GCP\njupyter\n\nProgramming: 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