AI Safety Argumentation Research Engineer
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Job Description
Research Engineer developing and operating systems for AI risk management and safety. Building ontologies and argumentation frameworks for stakeholder communication.
Responsibilities:
- Extend ontologies and knowledge graph schemas representing claims, evidence, argument structures, defeaters, and confidence
- Implement defeasible argumentation frameworks (e.g., ASPIC+, Dung-style, argumentation schemes) that capture both logical structure and vulnerability to rebuttal
- Operate and quality-control LLM-driven population pipelines, with cross-check scaffolds, provenance tracking, and human-in-the-loop curation
- Architect agent coordination patterns for multi-step research and population tasks, with robust error handling and graceful degradation
- Pre-harden argument structures by mapping the strongest counterarguments, steel-manned objections, and known defeaters
- Build export pipelines that translate structured argumentation into diverse communications formats across audiences and registers
- Maintain current awareness across AI safety, capabilities, and governance sufficient to know when new developments require graph updates, and to know where to find authoritative further detail
- Collaborate with communications staff and researchers to ensure outputs serve real persuasive needs
Requirements:
- Working familiarity with formal or semi-formal argumentation theory (abstract or structured argumentation, defeasible reasoning, dialectical models, or argumentation schemes)
- Experience with ontology engineering or knowledge graph development (OWL/RDF, property graphs, or equivalent)
- Operational experience with LLM agent systems: agent coordination platforms, prompt engineering at scale, and QC regimes for LLM outputs (adversarial probing, consistency checks, calibration)
- Fluent vibecoding practice: rapid prototyping and shipping with LLM-assisted development in production-adjacent contexts
- Substantive grounding in AI safety, AI governance, and current frontier-AI dynamics, with the literacy to locate authoritative sources on any sub-topic or human expertise in the space
- Familiarity with philosophy of science concepts bearing on evidence: defeaters, burden of proof, inference to the best explanation, underdetermination
- Good coding skills; comfort with graph databases or query languages
- Experience designing cross-check and verification scaffolds for unreliable automated processes
- Sound judgment about when a claim is well-supported versus when it needs hedging, further substantiation, or withdrawal
- Self-directed; strong written communication
- Graduate work or equivalent depth in argumentation theory, computational argumentation, epistemology, or philosophy of science
- Familiarity with AIF, Carneades, or comparable computational argumentation tools
- Track record in AI safety or governance (publications, policy work, or substantive community contributions)
- Background in argument mining, claim extraction, or stance detection
- Experience with debate formats or structured deliberation methods
- Understanding of motivated reasoning, belief change, and cognitive biases as they bear on communications strategy
- Open-source contributions in any relevant area.
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