TITLE: Technical Staff / Product

ENVIRONMENT: AI, Finance

LOCATION:  NY, NY

COMPENSATION RANGE:  $200,000.00 - $275,000.00   DOQ

DEGREE REQUIREMENTS:  Bachelors Degree in Computer Science

EXPERIENCE LEVEL: 4 – 10 years’ experience

NUMBER OF POSITIONS AVAILABLE: 2

POSITION SUMMARY:

About This 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. Started by acquiring firms managing over $1B in client assets, giving real advisors, real clients, and real financial outcomes to build against from day one.

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.
• Consumer portals. Where clients interact with their advisor, see their full financial picture, and engage on their own terms.
• The AI that powers all of it. From the tools and skills agents use, to how those agents are embedded in the product itself.

Example problems you’d work on:

• 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 where a stale answer is as harmful as a wrong one.
• Generative UI that doesn't break the advisor's flow. Our agents view and update the advisor's frontend in real time: dynamic scenarios, interactive plan adjustments, visualizations that shift as the conversation does. The challenge is ensuring agents can reliably render and manipulate our pages without feeling unpredictable. An advisor mid-call can't be troubleshooting their UI.
• An agentic planning engine that earns trust over time. A client about to retire wants to know if they can help their kids with a down payment. The agent has to hold a continuously updated model of each client's situation, surface the right adjustments at the right moment, and explain its reasoning in terms the advisor can relay in plain English—getting more accurate the longer it knows someone.
• A migration engine that makes every acquisition possible. Every firm runs on something different: a CRM last updated in 2011, a proprietary custodian export, a decade of notes in a shared folder. The engine has to ingest all of it and produce data clean enough to power the models we build on top. Get it wrong and the personalization doesn't work. Data quality is the moat; migrations are how we grow.

·         Tech stack : AI/ML, LLMs, Python, TypeScript, React