about 4 hours ago
Responsibilities
- Own large areas of the platform end to end, from design to production deployment.
- Work on knowledge representation systems, including ontologies and knowledge graphs.
- Design and implement RAG pipelines for data processing.
- Build and maintain integrations between retrieval and ML components.
- Develop context retrieval systems balancing recall, precision, latency, and cost.
- Create evaluation frameworks and metrics to measure system performance.
- Build reliable backend services and data pipelines for ML components.
- Deliver experiments and new capabilities quickly with high quality.
Requirements
- 5+ years of experience in building and deploying machine learning or AI systems.
- Master’s or PhD degree in Computer Science, Machine Learning, AI, or equivalent experience.
- Deep understanding of retrieval systems, RAG, embeddings, and knowledge representation.
- Experience with knowledge representation, semantic search, or agentic systems.
- Proficiency in Python, with a focus on production-quality code.
- Experience scaling or shipping products at high-growth startups.
- Ability to operate in ambiguous problem spaces with a balance of research and pragmatism.
- Strong communication skills for cross-functional collaboration.