Where this role sits

This role owns the heart of how Sapience AI reasons. You lead the neuro-symbolic approach that combines symbolic knowledge, structure, and rules with neural and language-model capability, the approach that lets the platform reason over a community's expertise with both flexibility and rigor.

You own the direction of the COGENT architecture: how it fuses the KO (knowledge object) graph and symbolic reasoning with neural models, how it stays trustworthy and explainable, and how it advances over time.

You lead a specialized team and partner across research, applied AI, and knowledge graph engineering to turn a hard idea into a working system at the center of MINERVA.
Why this role exists

Pure neural systems are fluent but can be confidently wrong, and they struggle to explain themselves. Professional communities cannot act on answers they cannot trust or trace. Collective intelligence needs reasoning that is both flexible and grounded.

Bringing symbolic structure and neural capability together is one of the hardest and most valuable problems in applied AI. It is also the core of what makes Sapience AI different.

The Head of Neuro-Symbolic AI owns that problem. You set the architectural direction, lead the team, and are accountable for reasoning that members can trust, trace, and act on.
What you will own (Areas of Responsibility)

You hold seven areas of responsibility across the neuro-symbolic architecture. Each one is yours to set direction on, build, and be accountable for.
1. Neuro-symbolic architecture and direction

• Own the direction of the COGENT architecture and how it fuses symbolic reasoning with neural and language-model capability.
• Set the approach to grounding, structure, and rules that keeps reasoning trustworthy.
• Decide what belongs in the symbolic layer, the neural layer, and the seam between them.

2. Reasoning over the knowledge graph

• Own how COGENT reasons over the KO graph to turn a community's expertise into answers members can act on.
• Partner with knowledge graph engineering on the structures reasoning depends on.
• Advance retrieval, inference, and symbolic reasoning together, not in isolation.

3. Trust, explainability, and provenance

• Make reasoning explainable and traceable, so members can see why the platform says what it says.
• Build the guardrails and provenance that let communities trust and verify answers.
• Reduce confident errors, and handle uncertainty honestly.

4. Research direction and applied advances

• Set the research agenda for neuro-symbolic methods at Sapience AI.
• Decide which advances are ready for production and which need more work.
• Keep the approach current with the field without chasing every trend.

5. Team leadership

• Build and lead a specialized team of researchers and engineers.
• Set a high bar for rigor and for shipping, and hold both.
• Grow the people who will define this area for the company.

6. Productionization with platform and applied AI

• Partner with platform and applied AI to run neuro-symbolic reasoning reliably and affordably in production.
• Balance ambition against the constraints of latency, cost, and scale.
• Turn architectural advances into dependable behavior in MINERVA.

7. Evaluation and measurement

• Own how the quality of reasoning is measured, including accuracy, groundedness, and trust.
• Build evaluation that reflects what communities actually need, not just benchmark scores.
• Use evidence to steer the architecture's direction.

AI-augmented ways of working

You lead work at the frontier of applied AI, and you use AI throughout it, to explore approaches, run experiments, and reason about results, while holding the rigor that this domain demands.

The standard is human in partnership: AI accelerates the work, you own the judgment, the interpretation, and the call. The people who create the most value here are not the ones producing the most output. They are the ones turning evidence into clear, durable decisions.
What this role is not

To keep the boundary clear:

• This is not a pure research role. You are accountable for reasoning that works in production, not only for publications or prototypes.
• This is not a general engineering leadership role. You own the neuro-symbolic architecture specifically, in partnership with the broader engineering organization.
• This is not a knowledge-graph-only role. You own reasoning over the graph and its fusion with neural methods, partnering with graph engineering on the structures.
• This is not a benchmark-chasing role. You are measured on trustworthy reasoning members can act on, not leaderboard scores alone.

What success looks like

We measure this role on outcomes the team can see:

• Trustworthy reasoning. The platform reasons over community knowledge in ways members can trust, trace, and act on.
• A working architecture. COGENT advances as a real system in production, not just on paper.
• Explainability. Answers carry provenance, and confident errors go down over time.
• A strong team. You build a group that sets the standard for this work.
• Production-ready advances. Research turns into dependable behavior at acceptable cost and latency.
• Honest measurement. Reasoning quality is measured against what communities actually need.

Who you are
Required qualifications

• Deep expertise in neuro-symbolic AI, knowledge representation, reasoning, or a closely related field.
• Experience combining symbolic methods with neural or language-model systems.
• A track record of turning advanced AI research into working systems.
• Strong grounding in knowledge graphs, retrieval, and modern LLMs.
• Experience leading researchers or engineers, or clear readiness to lead.
• Rigor about evaluation, groundedness, and honest handling of uncertainty.
• The ability to translate hard ideas for a broad technical audience.

Preferred qualifications

• A graduate degree or equivalent depth in AI, machine learning, computational logic, or a related area.
• Publications, patents, or shipped systems in neuro-symbolic or knowledge-based AI.
• Experience with explainable AI and provenance.
• Experience productionizing reasoning systems at scale.
• Domain understanding of professional communities or knowledge-intensive organizations.

How you work

• You lead with the member's need for answers they can trust, and name the real problem before the solution.
• You hold rigor and shipping in the same hand.
• You defend a clear point of view with evidence and change it with evidence.
• You treat trust, explainability, and honesty about uncertainty as first-order.
• You grow the people around you.

Skills & Competencies

• Neuro-symbolic architecture and knowledge representation.
• Reasoning over knowledge graphs, plus retrieval and inference.
• Fusing symbolic structure with neural and LLM methods.
• Explainability, provenance, and trustworthy AI.
• Evaluation design for reasoning quality and groundedness.
• Research leadership and productionization.
• Clear technical communication of complex ideas.

Services & Tools Experience

• Knowledge graph and symbolic reasoning systems, ontologies, and rule engines.
• Modern ML frameworks (PyTorch or equivalent) and LLM tooling.
• Retrieval, embeddings, and vector systems.
• Graph databases (for example Neo4j-class systems).
• Evaluation and experiment-tracking tooling.
• Python as the primary language.
• Ownership of the COGENT architecture and its integration with the KO graph and MINERVA.

Prior Experience & Background

• Prior senior work in neuro-symbolic AI, knowledge-based systems, or applied reasoning research.
• Experience shipping reasoning or knowledge systems in production.
• A background bridging research and engineering.
• Experience leading a team or a significant technical area is a plus.

Cross-functional partners

You work most closely with Research, Applied AI, Knowledge Graph Engineering, Platform Engineering, and Product. You are accountable for the COGENT architecture and how it turns the KO graph into reasoning members can trust inside MINERVA.
How we hire

We review every application, and we encourage you to apply even if you do not match every line above. Research shows that talented people, especially those from underrepresented communities, often hold back when they do not meet every qualification. If that is the only thing holding you back, apply anyway.

Compensation

Base Salary: $216,000 - $240,000 + early stage equity

Generous health and wellness benefits