PbAbout Ontologic Intelligence /b /ppLarge language models have impressive intelligence but remain limited in their reasoning capabilities. We believe causal reasoning and active inference will be a cornerstone of artificial intelligence. LLMs get causal questions right when the answer is already in their training data, but often fall apart on novel or complex systems. The industry is converging on the idea that LLMs need a causal substrate, but nobody has built a scalable industry-standard causal substrate yet. /ppbr/ppOntologic Intelligence is building scalable persistent causal reasoning infrastructure: the layer between raw data and AI that encodes cause-and-effect with uncertainty, provenance, and support for dynamic, messy real-world structure. With several provisional patents filed, we're founding a team now from Barcelona. /ppbr/ppbWhat you'd work on /b /pulliDevelop and implement novel methods for causal reasoning over dynamic systems with conflicting evidence /liliDesign and implement a causal graph compiler that ingests messy real-world data and produces structured, query-ready causal representations /liliAdvance the integration of persistent causal structure with large language models /liliDesign rigorous experiments to validate intervention and counterfactual queries over compiled causal graphs /liliContribute to patent-backed research on agentic causal graph reasoning, interpretation-conditioned inference, and Causal World Models /liliPublish and present research at top venues in causality, ML, and AI /li /ulpbr/ppbRequired Experience /b /ppbStrong background (PhD, MSc, or equivalent depth through self-directed work) in Computer Science, Mathematics, Physics, or a related quantitative field. /bWe care about what you've done and how you think, not where you went to school. Ideal backgrounds (any of these): /pulliCausal inference, structural causal models, causal discovery — you think in DAGs, SCMs, and interventions /liliProbabilistic graphical models, Bayesian inference, or information theory — you're comfortable with uncertainty as a first-class object /liliGraph systems, compiler infrastructure, or scientific computing /liliDeep learning research. You can train, fine-tune, and modify language model architectures, not just call APIs /liliNeurosymbolic AI — you've worked at the bridge between symbolic reasoning and neural computation /li /ulpbYou've built things. You have a personal site, a side project, a tool someone uses, an open-source contribution, a research background in AI or causality, or a startup attempt. /b /ppbr/ppbWhat this is (and isn't) /b /ppbThis is a founding role. Equity-only until we raise (targeting late 2026 / early 2027). No salary yet. You'd be joining a one-person team with a defined architecture, filed pending patents, and a clear thesis. /b /ppThis is for someone who wants to build something foundational, not someone looking for a comfortable job. If you're finishing a PhD, between positions, or working on something that isn't going anywhere and want to work on a problem that matters, this might be the right moment. /ppBased in Barcelona; remote works if you're a strong async communicator and self-starter. /ppbr/ppbHow to apply /b /ppbSend a note to or apply on LinkedIn. Tell me what you've built and why this problem interests you. /b /p