About the role
Responsibilities:
- Lead and transform: 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.