Member of Technical Staff, Product Engineer

About Arca Arca is a wealth management firm built from the ground up with AI. Most people get financial advice that's reactive: an annual check-in, a plan that's a document instead of a living thing, a relationship where you're one of three hundred clients your advisor is trying to remember. We think that's backwards. The kind of service that used to require a team of specialists behind you, the kind that makes you feel like the only person in the room, should be available to far more than the ultra-wealthy. So we're building it. We're not SaaS — we are the wealth management business, rebuilding it from the inside with AI. Our platform is an Iron Man suit for advisors: it takes over the low-leverage work so they can focus on what actually requires a human, showing up with empathy, context, and judgment. Underneath, it keeps a living understanding of each client. It remembers the thing you mentioned once, six months ago. It notices when your life changes — a new job, a new kid, a market shift — and adjusts before you think to ask. You won't see the technology. You'll just notice your advisor seems to know you better than any financial professional ever has. That's the product we're growing: client by client, on the strength of the experience itself. We started by acquiring firms managing over $1B in client assets, which gave us real advisors, real clients, and real financial [... source excerpt omitted ...] ys a week—lunch together, coffee breaks, basketball games, happy hours. The role As a Member of Technical Staff focused on Product, you’ll own entire product domains end to end. You'll work directly with our advisors, operators, and clients to understand what they need, then prototype, ship, and iterate fast. If you ship it, you own it What you’ll build (and own) Advisor and operator surfaces. The tools your colleagues use to serve clients, built so one advisor can deliver high-touch service to far more clients than they could before. This is how the firm scales without the experience degrading. Consumer portals. Where clients interact with their advisor, see their full fi [... source excerpt omitted ...] Example problems you’d work on These aren’t hypothetical; we’re actively working on versions of all of these. A research agent advisors actually trust. An advisor prepping for a client meeting needs to know how a pending divorce affects an estate plan, cross-referenced with tax law and the client's portfolio. The agent has to search proprietary data, integrations, and the web, then synthesize something the advisor can act on without second-guessing. "Close enough" isn't acceptable here. The hard part is building a system that knows what it doesn't know, and surfaces that uncertainty in a way that builds trust, under real constraints: compliance, auditability, and datasets whe