Principal Research Scientist (Neuro-symbolic AI)

Neuro-Symbolic AI Scientist

Company: Blue Fire AI

Team: Decision-Engine Research & Development

Location: Singapore (hybrid; some collaboration across Japan, UK and US time zones),

Employment Pass will be sponsored

Level: Senior / Principal Research Scientist

Reports to: CEO

Requirement: PhD, plus hands-on ability to write production-quality code and design

original algorithms

About Blue Fire AI

Blue 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 Role

You 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 Do

Expected || Reasoning architecture

• Design 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 machinery

• Implement 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 uncertainty

• Design 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.

Productization

• Own 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 Qualifications

• PhD 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:

o 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)

o Causal inference: SCMs, do-calculus, identification, counterfactual

identification, canonical representations, distributional regression

o Counterfactual learning systems: off-policy evaluation, importance

sampling and clipped estimators, confidence bounds, counterfactual

policy improvement

o Graph representation learning: GNNs, heterogeneous and knowledge

graphs, meta-paths, relational and combinatorial reasoning, graph

explainability

o Abductive and non-monotonic reasoning; logic programming; answer set

programming; probabilistic logic

o Multivariate time-series modelling, anomaly detection, and attribution

o Constrained 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 Qualifications• Publications 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 Candidates

1. 2. 3. 4. 5. Technical 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 Offer

• Ownership 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.