The Mission
We live in a paradox: AI is accelerating the world’s capabilities, yet the average person feels more financially precarious than ever. Inflation is rising, wages are stagnant, and the traditional "retirement" model is broken. We aren’t building another chatbot. We are building the Financial Answer Machine, an intelligent guide designed to help people navigate a new financial reality.
Underpinned by a proprietary financial system, we are turning "average" advice into personalized, multi-modal financial power. We have closed an over-subscribed seed round and looking for founding team members to help us build a bridge between the intelligence of AI and the rigid accuracy required for financial freedom. This is a rare opportunity to join at Day Zero and architect a business designed for outsized impact and massive scale.
The Role
Hence is hiring an early product engineer to own the product surfaces where people experience AI-powered financial planning. This is a frontend-focused role, but not a narrow frontend role.
You’ll lead the web and mobile experience across React, React Native, and Expo; shape new AI-native interaction patterns for financial planning; and flex into backend, infrastructure, agents, data flows, and product strategy whenever that is the shortest path to shipping something excellent.
You’ll work directly with the Head of Engineering as one of the first engineering hires. The right person is a builder with strong product taste, high agency, and a demonstrable track record of using AI and agentic engineering tools to ship better software faster.
What You’ll Do (The Day-to-Day)
- Own major parts of the Hence product experience across web, iOS, and Android.
- Design and build intuitive conversational, interactive interfaces for financial and life planning that feel like a partner, not a web form.
- Turn ambiguous product ideas into polished, production-ready experiences with strong UX, performance, accessibility, reliability, and observability.
- Build in TypeScript across the stack when needed, including frontend, API contracts, data fetching, auth flows, background jobs, and integrations.
- Help define our frontend architecture, component system, design patterns, mobile/web code-sharing strategy, and product engineering standards.
- Use agentic software engineering tools and workflows as real leverage, not as demos: prototyping, implementation, refactoring, testing, debugging, code review, documentation, and operational automation.
- Contribute to the product’s AI layer, including multi-model orchestration, LLM UX, evaluation loops, tool use, reliability, latency, cost, and failure-mode handling.
- Work closely with founders, customers, and early users. Ship quickly, watch what happens, and iterate.
What We're Looking For:
- You have built and shipped high-quality product experiences that real users depend on.
- You are excellent with React, TypeScript, modern frontend architecture, state/data management, and production UI performance.
- You have strong product and design judgment. You care about the difference between a screen that technically works and an experience that feels obvious, trustworthy, and fast.
- You are genuinely AI-native in how you build. You have a portfolio, shipped product, open-source work, technical write-up, or demo showing how you use AI/agentic systems to create meaningful leverage.
- You are comfortable moving beyond the frontend when needed: APIs, databases, auth, observability, infra, deployment, security, and product analytics do not scare you.
- You can operate in a seed-stage environment where requirements are incomplete, priorities change, and ownership is broad.
- You communicate clearly, write things down, make good tradeoffs, and prefer shipping small, high-quality increments over disappearing into large speculative rewrites.
- You work well in a remote, distributed environment: proactive communication, strong ownership, written clarity, and good judgment around when to sync vs. work async.
Nice to have
- Experience with React Native, Expo, TanStack Query/Router, Tailwind, Uniwind, HeroUI, Cloudflare, PostgreSQL, PlanetScale, Terraform/OpenTofu, or AI observability/evaluation tooling.
- Experience building financial, data-heavy, regulated, privacy-sensitive, or consumer trust-heavy products.
- Experience with LLM-powered UX, streaming interfaces, agent orchestration, tool-calling, evals, retrieval, structured outputs, or multi-model systems.
- Experience as a founder, founding engineer, product engineer, design engineer, or high-agency early startup builder.
Our stack
TypeScript across frontend and backend; React; React Native and Expo; TanStack Query, Router, and related tooling; Tailwind, Uniwind, and HeroUI; PlanetScale Postgres; deep Cloudflare integration across application, hosting, performance, and security; Terraform/OpenTofu; and multi-model agentic orchestration across Anthropic, OpenAI, and other model providers.
We do not expect you to have used every tool in our stack. We do expect you to learn quickly and make pragmatic decisions.
How we work
We are a fully remote, distributed team. Periodic in-person get togethers will be integral to our Operating cadence. We’re adults who prioritize outcomes & output over set schedules. We value clear writing, high ownership, fast iteration, direct communication, and thoughtful async collaboration.
As an early engineer, you should expect broad ownership, frequent context shifts, and a high degree of autonomy. You will help shape not just the product, but also the engineering culture, technical standards, and operating cadence of the company.
Compensation
Base salary: $150,000–$215,000, plus meaningful early-stage option equity.
Final compensation will depend on level, experience, location, and scope of responsibility.
This role is open to candidates based in the United States.
How to apply
Please include:
- resume or LinkedIn profile and a portfolio,
- GitHub,
- shipped product, demo, technical write-up, or other proof of work.
We are especially interested in seeing examples of AI-native or agentic engineering work. Helpful examples include products you shipped with LLMs, agent workflows you built, coding-agent workflows you rely on, complex prototypes you turned into production systems, or a write-up of how AI changed the way you designed, built, tested, or operated software.