AI Safety Argumentation Platform Research Engineer
New
Anywhere (Open Globally)Full-TimeMiddle
Salary160,000 - 210,000 USD per year
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Job Details
- Required Skills
- PythonPrompt Engineering
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
- 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
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
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