Logic & Reasoning Systems Engineer / Scientist

About us - Emergence AI is a frontier agentic AI lab advancing safe, autonomous AI through its pioneering neuroformal architecture. We are building the next generation of enterprise AI: systems capable of understanding complex knowledge, reasoning across information, executing multi-step workflows, and taking action with the verifiability and reliability required for mission-critical environments.
Our Enterprise Agent Platform provides the foundation for building, deploying, and operating AI systems in production — from data connectivity and knowledge management to workflow orchestration, observability, evaluation, and governance. We partner with organizations in semiconductor manufacturing, life sciences, and other data-intensive industries to solve complex challenges that require trusted AI systems capable of operating in real-world environments.
Our team brings experience from leading AI research labs and technology companies, including IBM Research, The Allen Institute for AI, Amazon, and Broadcom. With headquarters in New York and offices across California, Spain, and India, we bring together diverse expertise and perspectives to tackle some of the most challenging problems in enterprise AI.

ABOUT THE ROLE

Emergence.ai is building neuro-symbolic and neuroformal systems that bring together language models, explicit knowledge representation, formal reasoning, and trustworthy execution. We are looking for a technically deep engineer or scientist to apply formal methods and logical reasoning to production systems that people can depend on.

This role is about putting established reasoning technologies to work in the real world: Prolog, SMT solvers such as Z3, Lean, Datalog, answer set programming (ASP), description-logic reasoners, and related tools. The challenge goes beyond proving theorems. It is about making reasoning hold up reliably in the face of incomplete knowledge, exceptions, conflicting rules, and real computational limits. You will work closely with specialists in computational linguistics and ontology.

WHAT YOU WILL DO

• Build production systems that reason over ontology-grounded rules, axioms, policies, and domain knowledge, choosing Prolog, Z3/SMT, Lean, Datalog, ASP, or related tools where each fits best.

• Design practical reasoning strategies that tolerate exceptions, inconsistency, and incomplete information, drawing on closed-world reasoning, stratification, defeasibility, constraint solving, and contradiction detection.

• Implement deterministic or formally constrained SQL generation from grounded semantic representations.

• Build ontology-informed analytical systems for analytics, visualization, BI, root-cause analysis, and excursion detection.

• Combine symbolic reasoning with trusted statistical, ML, and algorithmic components where appropriate.

• Formalize computational components to enable trustworthy code generation through controlled composition, and work with ontology specialists to represent each component’s capabilities, requirements, constraints, and semantics.

• Turn advanced R&D concepts into robust commercial systems.

• Contribute to technical leadership and the development of novel methods, including publications and patents where appropriate.

WHAT YOU BRING

• Master’s or PhD in Computer Science, AI, Logic, Formal Methods, or a closely related field, pairing strong academic grounding with meaningful commercial experience.

• Deep expertise in symbolic reasoning, logic programming, or formal methods, with hands-on experience in one or more of Prolog, Z3/SMT, Lean, Datalog, ASP, or theorem proving.

• A practical understanding of reasoning under inconsistency, exceptions, and incomplete information, including defeasible and non-monotonic reasoning, closed-world assumptions, and stratified rule systems.

• Strong software engineering skills and a track record of integrating reasoning technology into production systems.

• Familiarity with ontologies and knowledge representation.

• Highly desirable: experience combining logical reasoning with trusted analytical, statistical, or ML components.

ESPECIALLY RELEVANT EXPERIENCE

Experience with rule engines, formal verification, deterministic SQL generation, ontology-informed analytics, policy reasoning, trustworthy code generation, or neuro-symbolic and neuroformal systems would be particularly valuable.