Arca Member of Technical Staff, Agent Platform

Source: Paraform - Company:

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 youll own Arcas 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 youll 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 advisors 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 dont have clean answers.

• 4 - 10 years of experience in full-stack or backend engineering building LLM-powered agent systems

• Comfortable where correct isnt 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