Founding Full Stack Engineer

Clera•San Francisco, CA

6d•$180,000 - $230,000•Onsite

About The Position

A well-funded, early-stage B2B SaaS startup in the AI-powered sales automation space is hiring its first Founding Engineer. You'll join the CTO as a core member of a tiny, high-caliber team and help set the technical culture for everyone who comes after. The company builds self-improving conversational agents that run product demos 24/7 — adapting to each buyer, handling technical questions, and getting smarter with every conversation. Growth has tripled month-over-month and demand is accelerating.

Requirements

Proven experience designing and building LLM-based conversational agents — orchestration, tool use, prompt engineering, and conversation flow.
Production backend development experience in Python (services, APIs, backend infrastructure).
Frontend experience with React — building interactive, adaptive UIs and rapid prototyping.
Demonstrated ability to deliver full-stack features end-to-end: frontend, backend, integration, and deployment.
Experience building evaluation pipelines, automated regression tests, observability tooling, and CI/CD workflows.
Experience designing deployment and versioning infrastructure for agents (feature flags, preview environments, versioned rollouts).
Experience with voice-enabled or browser-based agents (STT/TTS, WebRTC, or browser automation) or equivalent voice/browser + LLM integration experience.
Experience deploying and operating production services on cloud platforms (AWS or GCP) with Docker; Kubernetes or similar orchestration a plus.
Strong product sense: ability to rapidly prototype, iterate on feedback, and prioritize high-impact work in a fast-moving environment.
Experience at an early-stage startup or demonstrated ability to thrive in ambiguous, high-velocity environments.
Strong written and verbal communication skills — comfortable debugging issues with customers and documenting design decisions clearly.
Willingness to be available for urgent production issues and participate in on-call or incident response rotations.

Nice To Haves

Familiarity with reinforcement learning workflows (RL, RLHF, reward modeling) or experimentation frameworks applied to agent improvement.

Responsibilities

Own problems across the full stack, from LLM orchestration to embeddable frontend widgets to eval pipelines.
Develop and maintain the Agent: LLM orchestration, conversation flow, tool use, and voice.
Build and enhance the Experience: Embeddable, generative UI that adapts in real-time — fast-loading, interactive, and responsive across products and screen sizes.
Develop and maintain the Platform: Customer-facing tooling for configuring agents, managing flows, reviewing conversations, and measuring performance.
Implement Evals & Self-Improvement: Pipelines that measure agent quality and feed learnings back automatically.
Manage Infrastructure: Ensure zero cold starts, instant agent response, and versioning systems that let customers preview changes before they go live.
Debug conversation logs and trace LLM behavior.
Prototype generative UI widgets.
Trace quality regressions and implement automated tests.
Design agent versioning infrastructure.
Participate in on-call or incident response rotations.