СШАFounding SWE / MLE
4 нед. назад
250k–500k USD / годСШАSeniorRemote
llmlangchainlanguage modelssoftware deploymentpolicy evaluationsystem design
Founding software engineer role focused on building core enforcement engine for AI agents, deploying in customer codebases, and research on agent authorization.
Требования
- Deep with LLMs and agents, you understand tool calling, context, eval, and where agents actually break in production
- Strong systems engineer, low-latency and correctness-critical code doesn't scare you
- Excellent all-around SWE, you go from a vague problem to a working thing fast
- Comfortable straddling product and research, you want to build the thing and figure out the hard open problems behind it
- You move fast, have taste, and want to be early at something that matters
- Honestly, just be cracked at anything. If you’re really good at anything, please apply
- Bonus if you've built agent infrastructure, worked on security, verification, or formal methods, deployed into enterprise environments, or have research chops (papers welcome, not required).
- This is an in-person role in San Francisco / NYC. We're small, we're moving fast, and the ceiling here is enormous.
Другое
- is the runtime enforcement layer for AI agents. We sit between an agent and its tools, intercept every action before it executes, and check it against declarative policy, so agents can take real actions in production without doing the wrong thing. We're YC W26, we’re a group of researchers and engineers trying to combine both to get the best results in a productized manner.
- We're looking for a founding engineer who is genuinely cracked across three things most people are only good at one of: LLMs and agents, systems, and shipping real software. You'd be one of our first engineers, working directly with me on everything from the core product to deploying inside customer codebases to the research that keeps us ahead.
- Build the core enforcement engine, the thing that intercepts tool calls, evaluates policy deterministically, and returns something the agent can actually recover from
- Go into customer codebases and get deployed against real agent stacks (LangChain, LangGraph, MCP, whatever they're running)
- Work on the research side with us, benchmarks, adversarial evaluation, and the harder problems around agent authorization and provenance that our papers come out of
- Own real surface area from day one, this is a founding role, not a ticket queue