About the role
Source: Paraform - https://www.paraform.com/share/-arca/cmrwefqnt00000al0hakfqkrg · Company: arcawealth.ai
Location: On-site, 5 days a week in Flatiron, New York City · Full-time · Salary $200K - $300K plus competitive equity · Open to visa transfers (e.g. OPT, H1B transfers)
The role
As a Member of Technical Staff focused on Applied AI, you'll own Arca's AI stack end to end. One framing the team keeps coming back to: agents are the primary users of the system of record. Everything built (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.
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
- Human / AI collaboration in practice: how the agent updates its model of what an advisor wants when they override a recommendation, and learns what to do autonomously versus flag.
- Memory systems that know a client the way a great advisor does, across years, surfacing the right context at the right moment.
- Generative UI as an agent architecture problem: managing state as the agent updates the advisor's screen in real time, without the experience feeling unpredictable.
- Evaluations for financial services: high-accuracy financial data where errors have real consequences, plus judgment-heavy tasks, under compliance and auditability requirements.
Team
A 12-person team, one office, five days a week, working on problems that don't have clean answers.
Requirements
- 4 - 10 years of experience in full-stack or backend engineering, building LLM-powered agent systems
- Comfortable where "correct" isn't always obvious; energized by probabilistic systems and rigorous about evaluation infrastructure
- Careful with consequences: agents here touch real retirement savings and estate plans
- Moves fast and treats evaluation and iteration as part of shipping
- High bar for quality; spots AI slop instantly and leaves systems better instrumented than found
- Kind, direct, low-ego; gives candid feedback and thrives in an in-person team
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