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 and OpenAI's MCP -based financial workflows via Daloopa, 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.
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 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. It's the operations backend and tooling our analysts live in every day to review, correct, and enrich model output at scale, and it's where model uncertainty gets routed to the right human at the right moment. As a Senior Backend Engineer on Doppler, you'll own the systems that make that possible: the workflows that move extraction work through analyst review, the data model that keeps every correction traceable and audit -ready, and the performance and reliability the platform needs as we scale across every public market and accounting convention. When analysts are faster and the loop is more reliable, Claude for Financial Services official ChatGPT connector Fast Company's Most Innovative Companies 2026 list Fiscal.ai FinancialReports.eu models improve and coverage compounds; you build the foundation that makes all of it hold up under load.
If you're excited by AI systems where humans and models actually collaborate (rather than chatbots pretending to know things), and by the backend rigor it takes to run those systems at scale, this is the role
What You'll Lead and Transform
- Build and scale Doppler, the analyst operations platform that powers our human -in -the -loop process. The connective tissue between analyst corrections and our models.
- Design database schemas and architectural decisions for performance, scalability, and resilience across a domain that spans every public market and accounting convention.
- Build the routing and uncertainty -aware workflows that decide when a model can act alone and when an analyst needs to weigh in, keeping analysts fast and unblocked.
- Own the reliability and observability of the platform, including caching, asynchronous task queues, and the distributed systems that keep it responsive under heavy, concurrent analyst workloads.
- Turn analyst corrections into structured, traceable training signal that feeds the next model cycle, in partnership with the ML team.
- Champion clean, maintainable code through reviews, tests, and clear documentation.
What Sets You Up for Success
- 5+ years of professional backend engineering experience. Strong expertise in Python and Django (or equivalent backend frameworks).
- Deep understanding of relational and non -relational databases (MySQL, PostgreSQL, Redis, DynamoDB), including schema design and query performance under real load.
- Solid experience with distributed systems, caching, and asynchronous task queues (Celery or equivalent).
- Track record of building and operating high -throughput, reliable systems, and of leading backend projects end -to -end.
- Comfortable designing for observability, resilience, and graceful degradation in production systems that people depend on hour to hour.
- 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 throughput and data quality directly determine downstream outcomes.
- Experience integrating LLMs into production: model serving, inference APIs, uncertainty -aware routing.
- Experience extracting or processing structured data from messy real -world documents (PDFs, HTML, scanned content) at scale.
- 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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