Join an early-stage AI infrastructure startup as a Founding Engineer to build next-generation operating systems for data. You'll work on agent systems, semantic layers, and distributed runtime environments. This is a hands-on role with a founding team to shape the technical foundation.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Title: Founding Engineer (Agentic Systems & AI Infrastructure)
Location: Remote | Full-Time | Founding Team
About the Company
An early-stage AI infrastructure startup is building a next-generation operating system for data—combining dynamic ontology engines with advanced agentic systems capable of autonomous, transparent workflow execution. Backed by tier-one investors and already partnering with global enterprises, the company is defining a new category of AI middleware.
Role Overview
As a Founding Staff Engineer, you’ll take end-to-end ownership of core AI infrastructure and help shape the technical foundation from the ground up. This role is highly hands-on, working directly with the founding team to architect agent systems, semantic layers, reasoning engines, and distributed runtime environments.
What You’ll Work On
- Agent Infrastructure: Orchestration, memory, task planning, and execution logic using Python and open-source LLM frameworks.
- Ontology & Semantic Systems: Automated layers that unify structured and unstructured data.
- Agent Runtime: Planning, tool execution, distributed system observability, and robust runtime workflows.
- Data Infrastructure: Metadata systems, connectors, ETL pipelines, and vectorized storage.
- Reasoning Engines: Hybrid LLM + symbolic pipelines, task decomposition, and long-term memory.
- Engineering Culture: Define standards for code quality, system design, and technical velocity while contributing to early hiring.
What You Bring
- 5+ years in backend, infrastructure, or AI systems engineering.
- Strong Python skills plus experience with LLMs, vector DBs, LangChain, or custom agent tooling.
- Background in agentic systems, reasoning pipelines, or semantic/ontology-based architectures.
- Familiarity with knowledge graphs, schema reconciliation, or semantic modeling.
- Experience across modern data infrastructure (e.g., dbt, BigQuery, Airflow) and cloud environments (AWS/GCP).
- Prior startup or early-stage experience; strong preference for candidates who have built AI or data infrastructure at scale.
- A builder mindset: fast, hands-on, customer-centric, and comfortable operating in ambiguity.
Why Join
- Work on frontier AI infrastructure with real enterprise adoption.
- Founding-level ownership and decision-making influence.
- Move fast and solve complex technical problems with minimal bureaucracy.
- Backing from leading US and European investors with a $M
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