Founding Full Stack Engineer

Aurora

Presented by

Cora AI

Founding Full Stack Engineer (Applied AI)

I accept Aurora's terms & conditions and have read the privacy policy

Continue with Google

or

Sign up with Email

About the role

San FranciscoSouth Bay AreaLos AngelesRemote

Founding Full-Stack Engineer — Applied AI

California-based remote

• Bay Area preferred for occasional in-person collaboration
• Full-time

$160K–$250K base + competitive equity

The company

The company is building AI-powered customer lifecycle management for enterprise B2B teams.

The wedge is the post-sales lifecycle

onboarding, QBRs, renewals, and churn. That is where customer work becomes long-running, stateful, and operationally messy enough that most software starts to break down.

This is not a support chatbot layered onto existing tooling. The product is being built to reconstruct customer operations with AI agents, then make those workflows reliable enough for enterprise use.

The team is small and senior. It was founded in 2025, has five employees, and is backed by a $4M seed round from Emerson Collective, Acrew Capital, Recall Capital, and angels from LinkedIn, BetterUp, Glean, Scale AI, and Gusto.

Early customers include BetterUp, Fountain, Dialpad, Coursera, and more.

The founding team has built generative AI and automation platforms at BetterUp, LinkedIn, and Thoughtful AI.

The role

This is a founding full-stack role for an engineer who can move from product problem to shipped system without a large layer of process in between.

The backend is Python/FastAPI-heavy, the frontend is React, and the work spans customer-facing product, internal tooling, and the infrastructure that keeps agent workflows coherent.

You will work directly with the co-founders and customers to turn enterprise use cases into reusable product primitives.

Your decisions will shape not only the codebase, but also the engineering standards and product direction of the company.

The technical problem

The hard part is not generating a model response.

The hard part is maintaining account context, workflow state, and memory across long-running systems that need to behave consistently from one customer to the next.

That means building software around long-running agents, memory systems, knowledge graphs, and agentic workflows that can be inspected, modified, and trusted in production.

The platform sits at the intersection of product configuration, customer-specific behavior, and reusable infrastructure. Every implementation choice has to balance those three constraints without slowing the team down.

What you'll own

• Backend services and APIs: design and ship the systems that power customer lifecycle workflows.
• Frontend experiences: build React surfaces that make agent behavior, workflow configuration, and outcomes understandable to operators.
• Customer implementation work: work directly with enterprise teams to map requirements, pressure-test edge cases, and co-build custom agents and workflows.
• Core product primitives: turn one-off customer needs into reusable abstractions instead of accumulating bespoke logic.
• Agent and workflow infrastructure: own the systems around long-running agents, memory, knowledge graphs, and agent orchestration.
• Architecture decisions: choose the right boundaries between product, data, and infrastructure as the system evolves.
• Engineering culture: help define the bar for code quality, speed, and ownership in a five-person team.

Who this is for

You are likely a fit if you have

• 5–7 years of full-stack or backend-heavy product engineering experience.
• Shipped production systems where correctness, maintainability, and customer trust mattered.
• Built both backend services and frontend product surfaces, or are strong enough to operate across both without becoming shallow.
• Worked directly with customers or internal stakeholders to turn ambiguous needs into usable software.
• Comfort making architecture decisions with incomplete information and then owning the result in production.
• Experience with AI-enabled workflows, stateful systems, or software where behavior depends on context over time.
• The judgment to separate custom customer logic from reusable platform primitives.
• The ability to communicate tradeoffs clearly to founders, customers, and engineers.

Tech stack

• Backend: Python, FastAPI
• Frontend: React
• Product scope: customer lifecycle management, agent workflows, and enterprise implementations

The stack is intentionally narrow enough to keep the team focused on the product problem, but the role is broad enough that you will shape how the system is built.

Why now

The company already has named customers and a clear wedge in a painful part of the enterprise customer workflow.

The next phase is not just adding features. It is turning custom agent work into a platform with clean abstractions, reliable behavior, and enough product rigor to support more customers without rebuilding the system each time.

That makes this a high-leverage founding seat

the technical decisions made here will define how the product scales, how quickly the team can ship, and what kind of engineers the company attracts later.

This role is not for you if

• You want a narrow backend-only or frontend-only seat.
• You prefer fully specified tickets over ambiguous problem solving.
• You avoid customer conversations or implementation work.
• You want to spend most of your time on model research rather than product systems.
• You are not comfortable owning architecture choices after deployment.

Compensation and logistics

• Base salary: $160K–$250K
• Equity: competitive
• Location: California-based remote
• In-person collaboration: Bay Area preferred for occasional meetings with the team
• Visa sponsorship: not available; TN visas supported; no new H1Bs or H1B transfers
• Employment: full-time

About Aurora

Aurora helps exceptional engineers find the right role at some of the most ambitious startups worldwide.

We work with teams that value high ownership, strong technical standards, and clear impact.