Applied AI Engineer

Role overview

Build production-grade AI systems that turn raw financial data into clear, trustworthy equity research for individual investors. The work centers on agentic systems, prompt pipelines, retrieval layers, evaluation harnesses, and the orchestration that ties them together. This is a shipping position rather than a research role, with a weekly release cadence and direct feedback on whether the output is getting sharper.

Responsibilities

- Build and own the agentic systems behind the research product, from prompt design through orchestration - Develop retrieval-augmented generation pipelines, including vector search and structured outputs - Design and run evaluation frameworks that catch silent regressions in output quality - Ship improvements on a weekly cadence to real members - Reason carefully about failure modes and push back on prompt changes that degrade quality - Collaborate closely with technical founders and the machine learning team

Requirements

- 4+ years of software engineering with meaningful AI or LLM work in production - Deep Python proficiency, including agentic systems, structured outputs, and orchestration patterns - Hands-on experience with LLM APIs such as Claude, OpenAI, or comparable providers - Experience building retrieval-augmented generation pipelines and vector search - A history of building and running evaluation frameworks rather than relying on intuition - Prior work at an early-stage startup and comfort with remote work

Nice to have

- Background in financial data, equity research, or retail investing - Experience designing evals that non-technical stakeholders can interpret