Member of Technical Staff, Agent Platform

The role

As a Member of Technical Staff focused on Applied AI, you’ll own our AI stack end to end. One framing we keep coming back to: agents are the primary users of our system of record. Everything we build (the data models, the APIs, the UI) has to work for a non-human user that operates at scale, across every client, all the time. That’s a different design constraint than most teams are used to.

What you’ll build (and own)

• A general agent capable of complex, multi-step tasks — planning, sandboxed code execution, web search, retrieval — that powers a "do anything" experience for advisors.
• Ambient agents that act on behalf of clients and advisors: triaging email, processing meetings, drafting communications, surfacing what needs attention before anyone asks.
• The agent harness that orchestrates LLMs, context, tools, retrieval, and business logic into something coherent and reliable.
• Generative UI and human-in-the-loop interfaces where the agent and advisor genuinely collaborate, not just take turns.
• Evaluation infrastructure that holds two bars at once: high-correctness financial data and subjective, judgment-heavy tasks.

Example problems you’d work on

These aren’t hypothetical problems; we’re actively working on versions of all of these.

• Human / AI collaboration that actually works in practice. An advisor is mid-call when the agent surfaces a multi-step recommendation: rebalance, adjust the savings rate, revisit the estate plan. The advisor takes two steps and overrides the third. Now what? How does the agent update its model of what this advisor wants, present reasoning the advisor can relay without sounding scripted, and learn over time what to do autonomously versus flag? This is the flywheel: the tighter the collaboration, the more the agent can take on.
• Memory systems that know a client the way a great advisor does. A good advisor remembers that a client gets anxious when markets drop, cares more about the kids' college fund than their own retirement, and prefers plain-English summaries. Building memory that captures and evolves this understanding across years—and surfaces the right context at the right moment—is genuinely hard. The challenge is knowing what to retrieve, what's still relevant, and how to represent a person's relationship to money in a way an agent can use.