Clera - Founding Full Stack Engineer

• The Platform: Customer-facing tooling for configuring agents, managing flows, reviewing conversations, and measuring performance. • The Platform: • Evals & Self-Improvement: Pipelines that measure agent quality and feed learnings back automatically, so demos get smarter without manual intervention. • Evals & Self-Improvement: • Infrastructure: Zero cold starts, instant agent response, and versioning systems that let customers preview changes before they go live. • Infrastructure: • Day-to-Day Examples • Debug conversation logs, trace where an LLM lost the thread during a pricing objection, and ship a fix to the orchestration layer by lunch. • Prototype a generative UI widget in an afternoon based on a whiteboard sketch and have something demo-able by end of day. • Trace a quality regression to a prompt change, roll it back, and add an automated test to prevent recurrence. • Design agent versioning infrastructure from scratch so customers can safely preview updates. • 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. • Familiarity with reinforcement learning workflows (RL, RLHF, reward modeling) or experimentation frameworks applied to agent improvement.