# Member of Technical Staff, Agent Platform at Arca > Onsite · United States · Staff · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Aarca%3A214a5d9c-a046-4a4b-9cdb-b301a8a8e8cf (read 2026-11-09). ## Facts (observed) - Company: [Arca](https://alderhq.app/companies/arca) - Location text: New York - Where it can be done from (inferred): Onsite · United States (evidence: "Location: New York") - Posted: 2026-06-20 (142 days ago) - First seen by alder: 2026-11-04 · last verified: 2026-11-07 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/arca/214a5d9c-a046-4a4b-9cdb-b301a8a8e8cf - Skills listed (inferred): LLMs ## Pay and sponsorship - Posted pay: $200k–$300k per year (from the listing's pay field) - Market band for similar posted roles (New York, n 273): p25 $200k, median $234k, p75 $270k ## Description (the employer's text) ### 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 outcomes to build against from day one. But acquisition was the starting line. The bet is that an advisor backed by this platform delivers something good enough that clients come to us on their own. It's a $20T market, and we think it's ready to be rebuilt. — Rron, CEO ### The receipts Stage: Series A, $64M raised Backed by: General Catalyst, Index Ventures, Venrock Board & Advisors: Former CEO of Vanguard, Former CFO of Schwab, Founder of Altruist, Morgan Housel (author of The Psychology of Money) ### 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. ### 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. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aarca%3A214a5d9c-a046-4a4b-9cdb-b301a8a8e8cf and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.