Founding Full-Stack Engineer

Founding Engineer — Full-Stack

San Francisco, CA · On-site 4–5 days/week · Full-time

$200K–$250K base + meaningful founding equity

The company

This is an AI-native platform for financial advisors. The product is being built to reduce the manual work involved in portfolio management, data aggregation, analysis, and execution workflows.

The market is fragmented: custodial data, portfolio analytics, workflow tools, and execution systems live in separate places and do not compose cleanly. That creates reconciliation work, brittle integrations, and slow operational loops.

The company is a 4-person founding team with operators from fintech and AI backgrounds, backed by investors and advisors with deep domain knowledge.

The role

This is a founding full-stack role for someone who can own product and platform work in the same week: backend services, data pipelines, AI integration, and advisor-facing interfaces.

This is staff-scope ownership on a very small team. Architecture, implementation, product semantics, and operational reliability all sit in the same lane.

You will work directly on the systems that make the product usable in real financial workflows, with very little separation between design, build, and iteration.

The technical problem

Wealth management software has to work across inconsistent external data sources, workflow dependencies, and user actions that affect real money.

The core problem is not building a chatbot. It is making financial data, tools, and workflows compose reliably under real operational constraints.

That means handling authentication, synchronization, structured transformations, observability, and tool-calling in a system that has to be useful every day, not just impressive in a demo.

What you'll design and build

• Platform infrastructure and backend services: build the services that process financial data, run workflows, and keep the product reliable as usage grows.

• Custodian and provider integrations: connect to custodians, data vendors, and financial platforms; handle authentication, sync, retries, and real-time data flow.

• Data pipelines and normalization: transform raw financial data into structured models that support portfolio optimization, analytics, and AI reasoning.

• AI agent and tool-calling infrastructure: connect LLMs to portfolio analytics, data services, and execution workflows in a way that is observable and safe to operate.

• Advisor-facing product surfaces: ship interfaces that make complex portfolio workflows legible and usable without hiding the underlying system complexity.

• Observability and reliability: instrument the stack so failures are visible, diagnosable, and recoverable before they become user-visible problems.

What strong candidates usually bring

You are likely a fit if you have most of the following:

• Built and shipped production systems in TypeScript/Node.js, or a comparable backend stack with the same level of rigor.

• Owned complex systems end to end: APIs, data models, jobs, integrations, and the product surfaces that depend on them.

• Experience with data-intensive products where schema design, consistency, and query behavior materially affect the user experience.

• Experience shipping AI or ML systems into production, especially where the model is one component inside a larger software workflow.

• Comfort working with financial data, regulated workflows, or systems where correctness matters more than optimistic assumptions.

• Good judgment on what to build now versus what to harden later, without creating future rewrite work.

• The ability to turn ambiguous product goals into concrete architecture and implementation choices.

• A bias toward systems that are observable, maintainable, and operable by a small team.

Tech stack

• Primary: TypeScript, Node.js

• Cloud: AWS, GCP, Azure

You should be comfortable moving across application code and infrastructure, and you should care less about matching a framework than about building systems that are correct, debuggable, and easy to evolve.

Why this role is interesting now

The company is still small enough that the founding engineer will shape core product and infrastructure decisions, but the domain is already complex enough that weak foundations would create real drag later.

This is the stage where engineering choices define the product’s ability to handle integrations, workflow complexity, and AI-assisted decision-making without turning into a brittle system.

If you are looking for a role where architecture, product semantics, and reliability are inseparable, the scope here is unusually broad.

This role is not for you if

• You want a narrowly defined feature lane with little platform ownership.

• You prefer working only on frontend polish or only on backend infrastructure.

• You need heavily specified requirements before you can make progress.

• You are uncomfortable working in a domain where data quality and correctness are product requirements.

• You want an environment where the stack is already fully settled and the architecture is mostly done.

Compensation and logistics

• Base salary: $200K–$250K, with flexibility for exceptional candidates

• Equity: meaningful founding-level equity

• Location: San Francisco, CA

• Work model: on-site 4–5 days per week in the Bay Area

• Relocation: open to candidates willing to relocate

• Visa support: H1B transfers supported; new H1B applications are not currently supported

• Employment: full-time

Interview process

Typical process:

• Intro round — 30–45 min: background, scope, and how you think about building systems.

• First round — 1–2 hrs: technical depth across product, architecture, and implementation.

• Second round: further technical and team fit discussion.

• Final round — 60–90 min: closing conversation with the team.

• Reference checks: completed at the end of the process.

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 scope.