AI Engineer

Hmbl Tech; San Diego (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Hmbl Tech is seeking a Founding AI Systems Engineer in San Diego, California, to take ownership of the AI core platform, focusing on autonomous learning and operational memory. Your work will involve developing orchestration and decision intelligence systems aimed at enhancing enterprise capabilities.

Ideal candidates are excited about AI tooling with strong engineering skills in TypeScript and Node.js, and are committed to building meaningful, long-lived systems in an entrepreneurial environment.

Qualifications

• Experience in AI systems and operational memory.
• Strong engineering fundamentals in a modern typed stack.
• Hands-on experience with agent frameworks.

Responsibilities

• Own and develop the AI core of the platform.
• Build LLM orchestration and operational memory systems.
• Create agent frameworks and tooling to support enterprise contexts.

Skills

LLM orchestration

AI tooling

TypeScript

Node.js

Enterprise ontology induction

Decision intelligence

Tools

LangGraph

DSPy

Job description

We are looking for engineers excited about building long-lived AI systems, not chatbots. As a Founding AI Systems Engineer, you own the AI core of the platform: agents, tools, ontology generation, memory, retrieval, and evaluation. This is not a model-training role. The work is agent architectures, semantic retrieval, knowledge representation, enterprise data systems, decision intelligence, and autonomous learning from operational exhaust: the workflow steps, approvals, log entries, and data changes an enterprise produces as it runs.

Above all, you build in an LLM-first, reasoning-first way. Nearly everything you ship should make the platform a little more self-improving, the way Anthropic let Claude Code help write Claude Code.

Responsibilities

• LLM orchestration and tool calling: the frameworks StarLifter’s agents use to reason over enterprise context and act.
• Agent frameworks: agentic systems that observe signals, reason about decision patterns, and recommend or automate governed actions.
• Ontology generation: pipelines that induce a machine-readable model of each enterprise (its products, customers, orders, rules, and policies) from its own systems and knowledge bases.
• Embedding pipelines and knowledge graph integration: the semantic retrieval layer that serves enterprise behavior back to agents.
• Operational memory: the durable record of events, decisions, actions, and outcomes that lets the platform learn from every decision.
• Evaluation harnesses: the systems that score confidence, benchmark outcomes, and prove decision quality improves over time.
• Real depth in modern AI tooling: LLM orchestration, RAG architectures, retrieval systems, and knowledge graphs in production. This field is young (these tools are only a couple of years old), so we care more about how far you’ve pushed them, and how fast you learn, than about years on a résumé.
• Hands‑on with agent frameworks and orchestration tooling such as LangGraph and DSPy.
• Strong engineering fundamentals and comfortable in a modern typed stack (we build in TypeScript + Node). You write production-quality code and think about reliability and scale by default.
• Genuinely excited by enterprise ontology induction, operational memory, decision patterns, agentic systems, and learning from operational exhaust.
• The kind of engineer who tests assumptions and shows up knowing more than we do about deep AI. You’ll challenge our thinking and make the platform better for it.
• Intentional about your work and your career: you build meaningful, long-lived systems and stay to see them through, energized by the ownership and ambiguity of an early-stage company.
• A craftsperson and a colleague: low ego, high standards, excited to build alongside a small, senior, highly collegial team.

Nice to have

• Working familiarity with core enterprise business processes (Quote-to-Cash, Procure-to-Pay, Hire-to-Retire) and ERP data, enough to understand what the platform is reasoning about.
• Semantic modeling, metadata systems, or business-context modeling experience.
• Deep familiarity with enterprise integrations (SAP, Oracle, ServiceNow, Salesforce, Snowflake, Databricks).
• Prior founding-engineer or very-early-startup experience.

Equal Opportunity Employer

We are an equal opportunity employer and value diversity at our company. We prohibit any form of workplace discrimination based on race, color, ethnicity, national origin or ancestry, citizenship, religion, sex, sexual orientation, gender identity or expression, veteran status, marital status, pregnancy or parental status, or disability. Applicants will not be discriminated against based on these or other protected categories or social identities