About Daloopa, Inc.
The data layer behind how AI actually works in finance.
Daloopa turns messy financial disclosures into audit -grade, model -ready data used by institutions like Morgan Stanley. Its infrastructure sits behind flagship launches such as Anthropic's Claude for Financial Services and OpenAI's MCP -based financial workflows via Daloopa's official ChatGPT connector, giving AI agents direct access to verified, traceable fundamentals.
It doesn't just move data; it makes it usable for LLMs in high -stakes, regulated environments where accuracy, sourcing, and audit ability actually matter. This role in production AI for finance has been recognized by outlets like Fast Company's Most Innovative Companies 2026 list.
The Role
Daloopa's mission is to become the market leader in high -quality, actionable data for the world's top investment professionals. We're judged on three things: coverage, speed, and accuracy. Whether a hedge fund analyst can find the right number, get it fast, and trust it absolutely. The companies in our space (AlphaSense, , Refinitiv, ) mostly solve "find and surface." Daloopa is solving ground truth at scale. What makes us different is the loop. Our analysts review, correct, and enrich every extraction from financial documents. Those corrections become a training signal for the next generation of
our models. Models get sharper. Analysts get faster. Coverage expands, speed compounds, and accuracy keeps climbing in a way LLM -only competitors can't match. That's why Morgan Stanley trusts our numbers, and why this company has a moat.
Doppler is the platform that loop runs on, and its interface is where analysts spend their day. As a Senior Full stack Engineer on Doppler, you'll own the analyst -facing experience end to end. Primarily the data -dense UI our analysts use to review, correct, and enrich model output at scale, but also the backend behind it: the APIs, data model, and workflows that surface model uncertainty and keep every correction traceable and audit -ready. This role leans frontend, but you'll move comfortably across the stack, shaping the API contracts and data flows that make the interface fast and reliable.
The experience has to stay responsive while rendering large, Claude for Financial Services official ChatGPT connector Fast Company's Most Innovative Companies 2026 list Fiscal.ai FinancialReports.eu dense financial datasets, and every interaction has to make analysts quicker and the loop more reliable.
If you're excited by AI systems where humans and models actually collaborate (rather than chatbots pretending to know things), and by owning a feature from the UI down to the API and data model, this is the role
What You'll Lead and Transform
- Build and scale Doppler's analyst -facing interface, the tooling that powers our human -in -the -loop process.
- The connective tissue between analysts and our models.
- Design and build performant, responsive UI that stays fast while rendering large, dense financial datasets across every public market and accounting convention.
- Build the interaction and workflow UX that surfaces model uncertainty, makes corrections effortless, and keeps analysts fast and unblocked.
- Own features end to end, from the frontend through the APIs, data model, and backend workflows that power them, ensuring every correction is captured accurately and traceably.
- Partner with backend and ML on the systems that turn analyst corrections into training signals, and keep the platform reliable and observable under heavy, daily analyst use.
- Champion clean, maintainable code through reviews, tests, and clear documentation.
What Sets You Up for Success
- 5+ years of professional software engineering experience, with a full stack track record weighted toward the frontend.
- Strong expertise in JavaScript/TypeScript and React (or equivalent modern frameworks).
- Deep understanding of frontend performance: rendering large datasets, virtualization, memorization, and controlling unnecessary re -renders.
- Solid backend experience: building and consuming APIs, and working in Python/Django (or an equivalent backend framework).
- Working knowledge of relational databases (MySQL/PostgreSQL), caching, and asynchronous task queues (Celery or equivalent), enough to design and reason about the data behind the UI.
- Experience building complex, data -dense UIs (grids, tables, dashboards) that people rely on hour to hour.
- Track record of owning features end to end and leading projects across the stack.
- Genuine interest in the problem domain. Financial data, operations platforms, or human -in -the -loop AI.
Bonus Points for
- Experience building operational tooling or human -in -the -loop systems where speed, clarity, and data quality directly determine downstream outcomes.
- Experience with data visualization and rendering large, interactive datasets performance.
- Familiarity with design systems, accessibility, and building consistent, maintainable component libraries.
- Experience integrating LLMs into production: inference APIs, uncertainty -aware routing.
- Background in fin -tech, financial data, or other domains where data quality is mission -critical, and audit trails matter.
- Familiarity with the financial fundamentals landscape: 10 -Ks, 10 -Qs, transcripts, segment reporting, non -GAAP reconciliations.
- Prior experience at growth -stage startups where you've scaled systems through major growth phases.
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