As the Founding Engineer at Levangie Laboratories, Inc., you will architect the runtime and evaluation systems that allow AI agents to learn from experience. You will own the agent lifecycle, recovery, and observability surfaces, bridging the gap between research hypotheses and production reliability in a high-impact, San Francisco-based role.
Founding Engineer at Levangie Laboratories, Inc.
Join Levangie Laboratories, Inc. as a Founding Engineer to build the learning layer that allows AI agents to carry experience forward. Founded by a RAG pioneer, LLABS is creating "Causal Trajectory Learning" to ensure institutional experience survives model updates. You'll own the agent runtime and evaluation architecture, working directly with the founder in San Francisco to bridge the gap between AI research and production evidence. If you have built stateful production systems and want to shape the future of agentic infrastructure, this is a rare opportunity to define the learning substrate for enterprise intelligence.
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Location
San Francisco, United States
Compensation
$180k-$300k + Equity
Company
Levangie Laboratories, Inc.
Role overview
1-10 employees
Levangie Laboratories is an AI research and development company building cognitive infrastructure for autonomous work. Its technology enables agents to learn from operational experience, integrate into existing enterprise tools, and improve from outcomes and expert feedback. By turning completed work into reusable knowledge, LLABS is building agents that become more capable with every deployment while preserving the institutional intelligence organizations usually lose over time.
What you will do
- Architect agent lifecycle and recovery systems to preserve state and identity across request-scoped and long-running executions, including during failures or pauses.
- Develop observability, root-cause diagnosis, and evaluation surfaces that connect decisions and feedback across sessions to improve agent behavior and reliability.
- Transform complex enterprise workflows into standardized connectors, telemetry, and reusable platform capabilities, ensuring the system scales efficiently under real operating conditions.
Who this is a fit for
- Proven track record of building and operating consequential stateful production systems such as distributed systems, ML platforms, developer platforms, or agent runtimes.
- Strong proficiency moving between Python application code, APIs, and containerized infrastructure, with an ability to navigate TypeScript-based product surfaces effectively.
- Systems-oriented engineer who treats authentication, tenant isolation, and recovery safety as core product behaviors rather than secondary operational tasks.
Why this role is remarkable
- Work directly with founder Brayden Levangie, an early RAG pioneer, to shape the core architecture and build the engineering team for a category-defining platform.
- Experience a rapid path from research to production by testing systems hypotheses against real-world constraints and turning successful results into the company’s foundation.
- Architect the future of “Causal Trajectory Learning,” building durable memory infrastructure that ensures institutional knowledge is never lost during AI model transitions or session resets.
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