Data & AI · Full Time or Part Time · Hybrid & remote
Heliqon is the operating system for dynamic manufacturing. We help industrial operators orchestrate plants, supply networks, and people in real time — adapting to disruption without grinding to a halt. Where legacy MES, ERP, and PLM stacks impose one rigid data model on every customer, Heliqon gives each manufacturer the tools to define their own. Where rigid workflows break, ours bend. Our customers stop being the human glue holding their factories together.
You will architect the data, knowledge, and AI foundation of Heliqon. That means owning the framework manufacturers use to model their own operations (assets, processes, materials, people, constraints, outcomes), how those customer-defined ontologies are stored and queried at scale, and how language models reason over them. We are not shipping one canonical schema — we are shipping the system that lets every customer build their own. Every product surface — agentic flows, optimization, simulation, analytics — sits on what you build. You'll work directly with the founders and lead the technical direction of this layer.
Ontology framework and knowledge graph engine: The meta-model and authoring tools that let each manufacturer define their own entities, relationships, and constraints — including what primitives the framework exposes, how customer ontologies evolve and migrate, and how they stay coherent across the platform. LLM architecture and agentic flows: Selection, fine-tuning, retrieval, evaluation, and integration of foundation models, including how agents reason over the graph and execute actions in the system. Database and storage strategy: Graph, relational, vector, time-series — choosing the right tool for each workload and designing the boundaries between them. Data infrastructure: Ingestion, transformation, lineage, and governance for messy industrial data (PLCs, MES exports, ERP dumps, sensor streams, documents). Foundational research: Applying network theory, econometric methods, and optimization to problems other vendors solve with hand-coded rules. Technical leadership: Setting standards, reviewing critical designs, mentoring engineers, and partnering with the founders on what to build next.
Deep expertise in knowledge representation — ontologies, knowledge graphs, semantic modeling, and the meta-modeling layer above them. Strong grounding in network theory and graph algorithms — not just graph databases, but the math behind why graph structure matters for inference, planning, and decision-making. Hands-on experience architecting LLM systems at production scale including retrieval, evaluation, fine-tuning, and agentic patterns. Fluency across database paradigms: graph (Neo4j, TigerGraph, Neptune), relational (Postgres), vector (pgvector, Pinecone, Weaviate), and time-series. A research mindset — comfort going to the literature, prototyping novel approaches, and defending technical choices with evidence. A track record of building foundational systems that other teams build on top of — you think in primitives and design for extension. PhD, MS/MSc, or equivalent industry experience in a quantitative discipline (CS, applied math, systems engineering, physics, computational science, or operations research).
Domain experience in manufacturing, industrial operations, or supply chain. Background in optimization, simulation, multi-disciplinary design optimization, or operations research. Published research on graph methods, LLMs, agent-based modeling, or applied AI for decision-making. Experience scaling early-stage systems from prototype to enterprise deployment. Familiarity with industrial data standards (ISA-95, OPC UA, B2MML, MTConnect).
Manufacturing is the largest sector of the global economy and one of the least well-served by modern software. The companies that solve this will be defined by the quality of their data foundation. You won't be tuning someone else's stack — you'll be making the architectural decisions that determine what Heliqon can do for the next decade. If you've been waiting for a role where graph theory, ontology design, and frontier LLMs all matter at once, this is it.
Send your resume or CV to dave@heliqon.com, along with anything that helps us understand how you think — a paper, a project writeup, a system you architected, a thread you can't stop pulling on. This role is open to full-time or part-time arrangements. Heliqon is an equal opportunity employer.
Apply — dave@heliqon.com