Member of Technical Staff, Agent Platform

Arca, New York

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.

The team

We’re small on purpose and engineering heavy: about 80% of the team is technical. We’re based in NYC and hail from high growth startups like Stripe, Ramp, Rippling, Plaid, Doordash, & Glean.

We’re fully in-office in Flatiron, five days a week—lunch together, coffee breaks, basketball games, happy hours.

What you will do

A general agent capable of complex, multi-step tasks — planning, sandboxed code execution, web search, retrieval — that powers a "do anything" experience for advisors.

All jobs

Member of Technical Staff, Agent Platform

Arca, New York

The role

The team

We’re small on purpose and engineering heavy: about 80% of the team is technical. We’re based in NYC and hail from high growth startups like Stripe, Ramp, Rippling, Plaid, Doordash, & Glean.

We’re fully in-office in Flatiron, five days a week—lunch together, coffee breaks, basketball games, happy hours.

What you will do

A general agent capable of complex, multi-step tasks — planning, sandboxed code execution, web search, retrieval — that powers a "do anything" experience for advisors.

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.
Generative UI as an agent architecture problem. When an agent views and updates the advisor's screen in real time—rendering scenarios, adjusting visualizations mid-conversation, surfacing recommendations inline—the UI is constantly changing. The challenge is how the agent manages state across those changes and how you keep the experience from feeling unpredictable. When the visualization shows a client's actual retirement savings, the advisor can't be surprised by their own screen.
Evaluations that work for financial services. Most evals are built for tasks with a single right answer. Financial advice isn’t like that; the same recommendation can be right for one client and wrong for another, and “correct” often depends on context the eval harness doesn’t have. You’ll build evaluation infrastructure that can hold two different bars simultaneously: high-accuracy financial data where errors have real consequences, and judgment-heavy tasks where the right answer is subjective and the stakes are relational. Add to this the compliance requirements of financial services, where auditability isn’t optional and infrastructure has to handle large, constantly changing datasets where a stale answer can be as harmful as a wrong one.

Member of Technical Staff, Agent Platform

The role

The team

What you will do

Member of Technical Staff, Agent Platform

The role

The team

What you will do

What you need

The receipts

Example problems you’d work on

Why now is the moment to join

About Arca

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Arca