Aifund
Engineer in Residence: Syntra
Mountain View, CA · Contract
Sponsorship not specifiedDetected 96 days ago
Full-Stack DevelopmentNLPLLMsContract Management
About the role
- In-house legal teams are drowning in contract volume, compliance obligations, and cross-functional requests but legal headcount doesn't scale with company growth.
- A clause extraction and classification system that identifies key terms, obligations, risk provisions, and non-standard language with high precision.
- A risk scoring engine that flags contracts or clauses that deviate from company-approved templates or contain unusual terms.
Responsibilities
- Design the document processing pipeline for handling the full diversity of enterprise contract formats.
- Build extraction models that identify and classify contract clauses with precision high enough for legal teams to trust.
- Develop a risk assessment framework that compares incoming contracts against company playbooks and approved clause libraries.
- Implement a review workflow that integrates with existing legal tools (CLMs, matter management systems, email).
- Work with in-house legal design partners to validate accuracy and build trust in AI-assisted contract review.
- Hands-on experience building with LLMs, particularly for structured data extraction, classification, or reasoning tasks.
- Ability to build systems where precision matters. Legal teams need to trust the output, so false positive management is critical.
- Ability to build systems where precision matters.
- Background in document AI, OCR, or PDF processing pipelines.
- Founder or founding engineer experience building for knowledge worker workflows.
Requirements
- Experience with document processing, NLP, or information extraction from unstructured text.
- Helpful but not required
Compensation
- $10,000/month for 12 weeks ($30,000 total).
- This is a contract role during the residency.
Benefits
- If the build leads to a funded company, the next step is a founder-level role with meaningful equity upside.
Company info
- The goal is to pressure-test the idea quickly and honestly with real users and customers.
This listing is sourced directly from Aifund's careers page and normalized into a canonical job model.