Founding Full Stack Engineer
At a glance
Mid-level Full-stack Engineering role at Clera. San Francisco · full-time · $180,000–$230,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Build across the agent stack, from LLM orchestration and conversation flows to customer-facing configuration tools
- Create embeddable interfaces that adapt to different products and screen sizes, then prototype and ship improvements quickly
- Trace conversation failures and quality regressions, fix their causes, and add automated checks to prevent them recurring
- Develop evaluation pipelines and agent versioning so customers can assess changes before updates go live
- Keep production agents responsive through infrastructure work, including deployment and operational improvements
What you bring
- You have built LLM conversational agents, including their prompts, tool use, orchestration and conversation flow
- You have delivered production backend services in Python and interactive, adaptive frontends in React
- You can take full-stack features from implementation through integration and deployment
- You have built evaluation and regression-testing systems, observability, CI/CD, and deployment or versioning infrastructure for agents
- You are legally authorized to work in the United States
Who this fits
This founding, mid-level role suits a hands-on engineer who can move between backend, frontend and agent systems, prototype rapidly, and handle ambiguous work. You'll work on-site in San Francisco five days a week. The role includes availability for urgent production issues and participation in on-call or incident-response rotations; visa sponsorship is unavailable.
From the employer
About the Role
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.
What You'll Work On
There are no strict lanes here. You'll own problems across the full stack, from LLM orchestration to embeddable frontend widgets to eval pipelines. Key technical pillars include:
The Agent: LLM orchestration, conversation flow, tool use, and voice. Agents must understand unfamiliar products and explain them clearly to prospects with varying levels of context.
The Experience: Embeddable, generative UI that adapts in real-time — fast-loading, interactive, and responsive across products and screen sizes.
The Platform: Customer-facing tooling for configuring agents, managing flows, reviewing conversations, and measuring performance.
Evals & Self-Improvement: Pipelines that measure agent quality and feed learnings back automatically, so demos get smarter without manual intervention.
Infrastructure: Zero cold starts, instant agent response, and versioning systems that let customers preview changes before they go live.
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.
What We're Looking For
Required:
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 Have:
Familiarity with reinforcement learning workflows (RL, RLHF, reward modeling) or experimentation frameworks applied to agent improvement.
Compensation & Benefits
Salary: $180,000 – $230,000 USD annually
Early-stage equity commensurate with founding engineer role
Visa sponsorship is not available — candidates must be legally authorized to work in the United States
Location
This role is fully on-site in San Francisco, CA, five days per week. Remote or hybrid arrangements are not available for this position.
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