AI applied research lab building synthetic sapience; human-like AI. Backed by Y Combinator.
About the role
Ego AI is a YC-backed applied AI research lab building the behavioral infrastructure for AI companions and agents. We work at the intersection of real-time conversational instincts, memory, and persistent identity; the layer that makes AI feel genuinely alive. We're a small, fast-moving team and we're defining a new category of human-AI interaction.
What they're looking for
- 3+ years building backend or infrastructure systems, with real production ownership of stateful services or relevant projects that they have worked on
- Hands-on experience building LLM agent systems: tool-calling loops, defensive handling of model output, and step/token/cost budgets
- Deep grasp of durable execution: checkpointing, idempotency, outbox/step-log patterns, and the ability to reason precisely about failure windows (what if it dies after the side effect but before it is recorded?)
- A deliberate context-management strategy for long-horizon tasks, and the ability to defend its trade-offs rather than only describe it
- Strong API and data-model design, persistence behind clean seams, comfort being judged on docker compose up working cold from a README
- Clear written communication. Architecture docs a reviewer understands before reading the code. Honest scoping (what you cut and why)
More about this role
Member of Technical Staff: Agent Runtime (Ego) Location: San Francisco, CA (hybrid) · Reports to: Founding team
Ego AI is a YC-backed applied AI research lab building the behavioral infrastructure for AI companions and agents. We work at the intersection of real-time conversational instincts, memory, and persistent identity; the layer that makes AI feel genuinely alive. We're a small, fast-moving team and we're defining a new category of human-AI interaction.
Own the agent harness. Design and maintain our core agentic loop (plan → tool call → observe → repeat → finalize) as a small, legible state machine. Errors are first-class: malformed tool calls, hallucinated tool names, and throwing tools get fed back to the model. They never crash the loop.
Make execution durable. Build checkpointed, resumable sessions backed by a database: step logs with pending/committed status, idempotency keys on side-effecting tools, and a clear account of the at-least-once vs exactly-once boundary. A crash between the LLM call and the tool call, or mid-compaction, must never corrupt a session or double-fire a POST.
Solve long-horizon context. Own our compaction strategy: pinned goals, running...
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