Founding AI Systems Engineer

Levangie Laboratories•San Francisco, CA

2d•Onsite

About The Position

LLABS is building systems that learn from experience and carry it forward. We connect decisions, actions, outcomes, and feedback so useful experience can shape later work. Models will keep changing. The experience an organization earns should not disappear with each new model or session. LLABS is building a durable learning layer that connects decisions, actions, outcomes, and feedback so future agents can inherit reviewed experience and use it in the right context. That layer can become enduring infrastructure between frontier intelligence and the institutions putting it to work. We are hiring a founding engineer to make that architecture reliable under real operating conditions. Your first mission is to make the runtime measurably reliable across state, recovery, evaluation, and observability. From there, you will deepen the experience-learning loop and turn successful operational work into reusable platform capabilities. You will work directly with Brayden, shape the runtime and evaluation architecture, and help build the engineering team around it. The path from a research idea to production evidence is short: you can test a systems hypothesis, see how it survives real operating constraints, and turn what works into the foundation of the company. LLABS has built an agent runtime, tool system, governed experience layer, and customer-review surface. The current engineering substrate is primarily Python and TypeScript, with API services, a React/Next.js product surface, relational and cache/storage layers, event-driven execution, containerized cloud infrastructure, and model-provider interfaces. Those components serve one architectural bet: the model can change, but useful operational experience should persist above it and remain scoped to the right user, role, workflow, and permission boundary. We call the learning architecture Causal Trajectory Learning. It keeps context, decisions, actions, outcomes, and feedback connected so reviewed experience can improve later work in the right scope. The next phase is to make that learning loop easier to evaluate, operate, recover, and trust. LLABS exists to compress the distance between a breakthrough and the work it changes. Enterprise environments are our proving ground because they concentrate the conditions a learning system has to survive: old software, long-running state, strict permissions, failure, and expert judgment. Research questions, runtime behavior, evaluation, and deployment evidence therefore live in the same engineering loop. That loop connects experiments about experience representation with runtime failures, expert-designed evaluations, and reusable platform changes learned from deployment. We want that learning infrastructure to help expert teams move faster on difficult scientific, industrial, and institutional problems, with each new agent and model generation inheriting the experience earned before it. This role sits at the center of that work. You will help build a system that survives real tools, permissions, failures, corrections, and users. Each experience should make it more capable.

Requirements

Built or operated a consequential distributed system, ML platform, developer platform, agent runtime, or other stateful production system.
Can show a failure you traced across several layers and explain the evidence that changed your diagnosis.
Designed an evaluation, experiment, or observability system that changed a product or architecture decision.
Comfortable moving between Python application code, APIs, queues, databases, containers, infrastructure, and a TypeScript product surface.
Treat authentication, permissions, isolation, secrets, recovery, and release safety as product behavior.
Worked directly with demanding users or customers and turned their reality into a general system.
Can disagree clearly, simplify a system, and stay hands-on when the failure is ambiguous.
Want research claims to survive production evidence, and want production evidence to change the research.
Experience with agent systems, ML systems, distributed systems, agent runtime, and evals.
Experience with Python and TypeScript.
Experience with API services, React/Next.js product surface, relational and cache/storage layers, event-driven execution, containerized cloud infrastructure, and model-provider interfaces.
Experience with Causal Trajectory Learning architecture.

Nice To Haves

Research papers, open-source work, founding experience, and unusual side projects can all be strong evidence.

Responsibilities

Make the runtime measurably reliable across state, recovery, evaluation, and observability.
Deepen the experience-learning loop and turn successful operational work into reusable platform capabilities.
Own agent lifecycle and recovery across request-scoped and long-running execution, with identity and state preserved through pause, resume, failure, and termination.
Own the experience and evaluation surfaces that connect decisions, actions, outcomes, and feedback across sessions and model changes.
Own observability, root-cause diagnosis, release gates, rollback, and recovery across models, memory, tools, permissions, runtime state, and product behavior.
Own the technical path from a selected enterprise workflow into standard connectors, tests, telemetry, and reusable platform capabilities.
Partner on tenant isolation, authentication, secrets, data boundaries, and the operational controls required for consequential enterprise work.
Partner on learning-system experiments, expert acceptance criteria, and the translation of evaluation results into research decisions.
Partner on workflow mapping and technical reviews with the people responsible for the customer relationship, commercial outcome, and trust decisions.
Shape the runtime and evaluation architecture.
Help hire the engineering team.
Have direct authority over the assigned technical path.
Turn promising ideas into dependable platform capabilities by testing systems hypotheses and seeing how they survive real operating constraints.
Diagnose and unblock paths, increasing founder leverage.