Principal Scientist (Neuro-Symbolic AI)

Company: Blue Fire AITeam: Decision-Engine Research & DevelopmentLocation: Singapore (hybrid; some collaboration across Japan, UK and US time zones), Employment Pass will be sponsoredLevel: Senior / Principal Research ScientistReports to: CEORequirement: PhD, plus hands-on ability to write production-quality code and design original algorithmsAbout Blue Fire AIBlue Fire AI is a technology-led asset management and investment intelligence firm. Our core platform, Emmalyn, is a neuro-symbolic fundamental-analysis engine. Emmalyn operates in two commercial modes: Risk Analyst, producing decision-ready outputs for investment managers and exclusion lists; and Investment Manager, where the engine drives allocation through a risk-alpha overlay.We are not building a black-box predictor. Every risk call we ship must carry an auditable chain of evidence: which entities, which relations, which filings, which events, and what would have had to be different for the call to flip. That requirement — machine reasoning that is both learned and explainable — is why this role is crucial.The RoleYou will design and build the next version of the reasoning core of Emmalyn: the layer that combines learned representations over company, executive, board, supply-chain and market graphs with explicit symbolic constraints, causal structure, and counterfactual evaluation. Concretely, you will turn methods from the current neuro-symbolic and causal-reasoning methods into production algorithms that generate, explain, and stress-test equity risk and alpha alphas across a global universe of thousands of listed companies.This is a research role with shipping obligations. You will publish internally, defend your methodology to portfolio decision-makers and institutional clients, and own the code that runs in the pipeline with implementation support from the AI development technologists.What You Will DoExpected || Reasoning architectureDesign the hybrid learning-and-reasoning core: differentiable logic layers (e.g. Logic Tensor Networks / Real Logic-style grounding of first-order signatures onto data), energy-based logical inference, and constraint injection into neural models so domain rules — accounting identities, governance rules, index methodology, exclusion policy — are enforced rather than hoped for.Build System 1 / System 2 style architectures: fast learned screening over the full universe, with slow, deliberate symbolic verification and meta-cognitive arbitration deciding when a case must escalate to the reasoning layer.Develop knowledge representation for our knowledge graphs, and reason over them with graph neural networks used as neuro-symbolic machinery (relational reasoning, constraint satisfaction, structured inference) rather than as generic embedders.Expected || Causal and counterfactual machineryImplement counterfactual reasoning on top of structural causal models — identification, interventional vs. counterfactual layers, canonical/normalized representations that separate unfalsifiable counterfactual assumptions from testable interventional constraints — and make explicit which of our claims live on which rung of the causal ladder.Build counterfactual evaluation for our own learning systems: off-policy / counterfactual estimation of "what would this alpha set have produced had we changed the rule," importance-weighted estimators with confidence intervals, and equilibrium-aware reasoning about a deployed system interacting with its environment.Develop anomaly attribution for multivariate time series using counterfactual replacement of variable subsets — moving us from "an anomaly fired" to "these three drivers explain it, and here is the evidence."Build evidence subgraph extraction for financial risk: Granger-causality-style meta-path attribution, edge-type-aware generators, feature masking, and joint counterfactual-plus-factual objectives so each risk flag ships with a minimal sufficient and necessary subgraph as its explanation.Aspirational || Abduction and uncertaintyDesign abductive inference for event and narrative reasoning — given an observed outcome and prior context, generate and rank the most plausible explanatory hypotheses, including backprop-based or search-based decoding that conditions on both past and future context.Represent non-probabilistic uncertainty where probability is the wrong tool: logic programming with three-valued/Kleene semantics, default and non-monotonic reasoning, normative and I/O logic for policy and mandate constraints.Build calibration and predictive-uncertainty layers, and explicit tests for out-of-distribution failure and for cases where theoretical guarantees break.ProductizationOwn the algorithms and the reasoning trace that makes each defensible to an institutional client reviewer.Work with the alpha research team on evaluation: CAR-based backtests, hit rates, cohort cuts, borrow-cost-adjusted net edge, capacity analysis, and honest median-versus-mean reporting.Write clean, tested, reproducible Python; contribute to pipeline design; leave documentation a colleague can rebuild from.Required QualificationsPhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Mathematics, Operations Research, Computational Logic, Econometrics, or a closely related field, with a dissertation or publication record in reasoning, causality, logic, or structured/relational learning.Demonstrated ability to design algorithms from first principles — not only apply libraries. We will ask you to derive, prove or bound something on a whiteboard and then implement it.Strong engineering ability: expert Python; fluency in at least one deep learning framework (PyTorch, JAX, or TensorFlow); comfort with SQL/Aurora and large tabular/time-series data; version control, testing, and reproducible experiment discipline.Depth in at least three, and working literacy across most, of the following:Neuro-symbolic / neural-symbolic computing: differentiable logic, fuzzy/many-valued semantics, knowledge injection, rule extraction, energy-based logical inference (e.g. RBM/Logical Boltzmann-style systems)Causal inference: SCMs, do-calculus, identification, counterfactual identification, canonical representations, distributional regressionCounterfactual learning systems: off-policy evaluation, importance sampling and clipped estimators, confidence bounds, counterfactual policy improvementGraph representation learning: GNNs, heterogeneous and knowledge graphs, meta-paths, relational and combinatorial reasoning, graph explainabilityAbductive and non-monotonic reasoning; logic programming; answer set programming; probabilistic logicMultivariate time-series modelling, anomaly detection, and attributionConstrained optimization and its integration with learning (MILP, robust/stochastic optimization)Explainability as a design goal — you have built systems where the explanation was a first-class output with its own evaluation metrics.Ability to read a research paper on Monday and have a defensible prototype by Friday.Clear technical writing and the ability to present methodology to non-AI experts, including investment professionals and clients.Preferred QualificationsPublications at NeurIPS, ICML, ICLR, AAAI, IJCAI, KDD, ACL, UAI, CLeaR, or equivalent venues in neuro-symbolic AI, causality, or graph reasoning.Experience in finance: equity fundamentals, credit or governance risk, factor/alpha research, event studies, index methodology, or regulatory filings (10-K/20-F/annual reports and disclosure regimes).Experience with NLP over long financial documents: event extraction, entity linking, transcript and disclosure analysis, and schema-bound information extraction.Familiarity with neuro-symbolic toolchains (LTN, DeepProbLog, Scallop, PyReason, NeurASP, ProbLog, Answer Set solvers) and graph stacks (PyG, DGL, Neo4j).Knowledge-graph construction at scale, including entity resolution across executives, boards, subsidiaries and dual listings.Experience taking research into production: cloud pipelines (AWS), batch and streaming orchestration, and monitoring for model drift.Meta-cognition, self-monitoring, or agent-architecture research — an acknowledged gap in the current neuro-symbolic literature and an area we intend to lead in.How We Evaluate CandidatesTechnical screen — your research: what you built, what you proved, what broke.Algorithm design exercise — design a reasoning layer for a stated financial risk problem, on a whiteboard, with your assumptions made explicit.Code exercise — implement a small differentiable-logic or counterfactual-attribution component and evaluate it honestly, including failure modes.Paper deep-dive — pick one paper from the neuro-symbolic or causal literature and teach it to us, including its weaknesses and what you would do differently.Investment-facing conversation — explain your method to someone who allocates capital and cares about auditability.What We OfferOwnership of the reasoning core of a live investment engine, with real capital and institutional clients downstream of your work — not a research sandbox.Proprietary datasets: a multi-year global company, executive and board knowledge graph, event and downgrade histories, and alpha outcome data with forward return windows.A small, senior, low-bureaucracy team; direct access to the founder; freedom to publish where it does not compromise IP.Competitive base, performance-linked compensation tied to engine and product outcomes, and support for conference attendance and continued research.