Agent systems · ML systems · distributed systems · agent runtime · evals

Build the learning layer that lets agents carry experience forward.

Location: San Francisco, five days per week in person
Base salary: $180,000–$300,000
Equity: 0.5%–2.0% target initial ownership on a fully diluted basis
Relocation: Up to $15,000 for candidates moving to the Bay Area
Benefits: $1,000 monthly healthcare stipend initially, plus flexible PTO
Visa: Sponsorship considered case by case
Travel: Up to 10%

Frontier models can reason, use tools, and complete increasingly complex work. But the systems around them still lose the most valuable part of doing the work: what decision was made, what happened next, which correction mattered, and where that lesson should apply.

RAG retrieves information. 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.

Why this is a rare seat

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.

What exists today

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.

What you will own

• Agent lifecycle and recovery across request-scoped and long-running execution, with identity and state preserved through pause, resume, failure, and termination.

• The experience and evaluation surfaces that connect decisions, actions, outcomes, and feedback across sessions and model changes.

• Observability, root-cause diagnosis, release gates, rollback, and recovery across models, memory, tools, permissions, runtime state, and product behavior.

• The technical path from a selected enterprise workflow into standard connectors, tests, telemetry, and reusable platform capabilities.

What you will partner on

• Tenant isolation, authentication, secrets, data boundaries, and the operational controls required for consequential enterprise work.

• Learning-system experiments, expert acceptance criteria, and the translation of evaluation results into research decisions.

• Workflow mapping and technical reviews with the people responsible for the customer relationship, commercial outcome, and trust decisions.

Team and scope

You will be a founding technical lead reporting directly to founder Brayden Levangie. You will shape the runtime and evaluation architecture, help hire the engineering team, and have direct authority over the assigned technical path. Brayden built one of the earliest production RAG platforms before the category had a name, architected the LLABS runtime, and sold the company’s first pilots.

Most of your time will go to the core platform and its research and evaluation loop. Selected customer workflows create the empirical pressure that turns promising ideas into dependable platform capabilities.

On-call is shared with the founder. Deployment work feeds reusable improvements into the common platform.

Hiring process

The process includes two focused conversations followed by a paid working session. We will discuss mutual fit and systems you have personally built, then agree on a staging problem, compensation, and what strong work looks like.

Problems you might work on

• What state must survive when an agent works for hours across files, APIs, browsers, databases, and subordinate agents?

• What should an agent preserve from a successful trajectory, and what evidence shows that lesson generalizes beyond the original session or workflow?

• How do we find the component that owns the broken state when the UI says a job is running but execution has stopped somewhere else?

• What evaluation predicts whether a domain expert will accept the completed work?

• How should feedback change later behavior while preserving workflow and tenant boundaries?

What success looks like in the first six months

• System: Important agent lifecycle and execution paths are observable, repeatably tested, and recoverable. Failures can be isolated to the right layer quickly.

• Workflow: one selected workflow has a dependable LLABS-owned path to production acceptance, with the critical permissions, evaluations, release checks, and recovery behavior made explicit.

• Compounding: at least one connector, evaluation, telemetry, runtime, or recovery capability learned from that workflow is reused in a second workflow.

• Learning: a reviewed outcome improves later behavior in the intended workflow and permission boundary.

• Team leverage: the engineer independently diagnoses and unblocks that path, increasing founder leverage.

Evidence that you could be exceptional here

We care most about what you have personally made work.

• You have built or operated a consequential distributed system, ML platform, developer platform, agent runtime, or other stateful production system.

• You can show a failure you traced across several layers and explain the evidence that changed your diagnosis.

• You have designed an evaluation, experiment, or observability system that changed a product or architecture decision.

• You are comfortable moving between Python application code, APIs, queues, databases, containers, infrastructure, and a TypeScript product surface.

• You treat authentication, permissions, isolation, secrets, recovery, and release safety as product behavior.

• You have worked directly with demanding users or customers and turned their reality into a general system.

• You can disagree clearly, simplify a system, and stay hands-on when the failure is ambiguous.

• You want research claims to survive production evidence, and you want production evidence to change the research.

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

Why LLABS

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.

Apply

Email brayden@levangielaboratories.com with examples of systems, research, or open-source work that show how you think. Include a short note about the hardest stateful or ML system you have personally made work.