Senior Backend Engineer - ML / LLM

As a Senior Backend Engineer on the ML side, you'll build the platform that makes the loop work. The systems that turn analyst corrections into structured training signal. The pipelines that move PDFs and HTML filings into model-ready data, route extraction work to humans when models are uncertain, and feed the result back into the next training cycle. The domain spans every public market, every accounting convention, every reporting nuance across geographies, and the platform you build has to scale across all of it.

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 the extraction pipelines that turn PDFs and HTML filings into structured, audit-ready data, in partnership with the ML team.
• Build the 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 our 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: model serving, inference APIs, 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. Financial data, supervised learning systems, or human-in-the-loop AI.
• You should be the kind of person who reads the AlphaSense product launches and has opinions.

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: 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.