Join an early-stage AI platform transforming financial workflows by implementing cutting-edge research into production systems. You'll build agentic AI, retrieval pipelines, and knowledge graphs to surface critical insights from unstructured data like PDFs and emails. This role offers hands-on impact, rapid iteration with customer feedback, and ownership of end-to-end AI deployment in a high-growth environment.
Key Responsibilities:
- Implement and adapt cutting-edge AI research to build systems that understand financial context and business concepts
- Design retrieval pipelines that extract and synthesize insights from unstructured sources with intelligent conflict resolution
- Build agentic workflows that enable AI systems to reason about financial relationships, perform benchmarking, and conduct comparative analysis
- Construct and maintain knowledge graphs that capture complex financial relationships, entity connections, and market dynamics
- Create evaluation frameworks to measure and improve AI performance across investment research, due diligence, and portfolio monitoring
- Design systems that maintain traceability from AI outputs to primary sources
- Experiment with latest LLM orchestration patterns and context management techniques for financial use cases
- Deploy and operate production systems end-to-end with ownership over reliability, performance, and maintainability
3+ years of experience in machine learning, AI research, or applied AI engineering
Strong background in LLMs, RAG systems, and agentic AI workflows
Experience designing evaluation frameworks and benchmarks for AI systems
Interest or understanding of financial markets, investment processes, or quantitative finance
Experience with retrieval systems, embeddings, and multi-modal data processing
Entrepreneurial mindset with ability to translate cutting-edge research into practical business value
Preferred:
Publication record or demonstrated expertise in relevant AI research areas
Experience as a full-stack engineer (React/TypeScript + Python/Node or similar)
Experience at an early-stage, high-growth startup
Degree from a top CS program
Research publications in relevant areas