Senior Backend Engineer - ML / LLM

As a Senior Backend Engineer on the ML side, you'll build the platform that makes this loop work: the systems that turn analyst corrections into structured training signals, the pipelines that move PDFs and HTML filings into model-ready data, the route extraction work to humans when models are uncertain, and the feed of the results back into the next training cycle. If you're excited by AI systems where humans and models actually collaborate, this is the role.

Responsibilities:

• Build and scale the supervised learning platform that turns analyst corrections into training data, the connective tissue between our human-in-the-loop process and our models.
• Design and operate extraction pipelines that turn PDFs and HTML filings into structured, audit-ready data, in partnership with the ML team.
• Build routing and uncertainty-aware systems that decide when a model can act alone and when an analyst needs to weigh in.
• Integrate LLMs and ML models into production via API endpoints and inference pipelines, with the reliability and observability clients require.
• Define database schemas and architectural decisions for performance, scalability, and resilience across a domain that spans every public market and accounting convention.
• Champion clean, maintainable code through reviews, tests, and clear documentation.

Requirements:

• 4+ years of professional backend engineering experience.
• Strong expertise in Python and Django (or equivalent backend frameworks).
• Hands-on experience integrating ML models or LLMs into production, including model serving, inference APIs, and vector stores.
• Deep understanding of relational and non-relational databases (PostgreSQL, MySQL, Redis, DynamoDB).
• Experience with distributed systems, caching, and asynchronous task queues (Celery or equivalent).
• Track record of leading backend or ML-integrated projects end-to-end.
• Genuine interest in the problem domain of financial data, supervised learning systems, or human-in-the-loop AI.

Bonus Points for:

• Experience building human-in-the-loop or active learning systems where labeled data quality directly determines model performance.
• Experience extracting structured data from messy real-world documents (PDFs, HTML, scanned content at scale).
• Background in fintech, financial data, or other domains where data quality is mission-critical and audit trails matter.
• Familiarity with the financial fundamentals landscape, including 10-Ks, 10-Qs, transcripts, segment reporting, and non-GAAP reconciliations.
• Prior experience at growth-stage startups where you've scaled systems through major growth phases.