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.
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