Related skills
nlp python rag information retrieval vector databases📋 Description
- Design and evaluate information access + reasoning across RAG, agents, and ML.
- Prototype GenAI workflows mapping controls <-> risks <-> requirements.
- Explore ML + probabilistic approaches where GenAI isn't best fit.
- Build and maintain evaluation frameworks: golden datasets and metrics.
- Implement and tune ranking/reranking systems: cross-encoders, LLM rerankers.
- Collaborate with AI and software engineers to productionize validated approaches.
🎯 Requirements
- 5+ years in applied research, data science, or ML engineering (NLP/IR focus).
- Strong IR foundation: dense and sparse retrieval, embeddings, search relevance.
- Experience with RAG: chunking, vector databases, retrieval optimization.
- Evaluation methodology: metrics design, golden datasets, A/B testing, statistical analysis.
- Strong Python skills; notebook-driven research workflows.
- Experience communicating findings to engineers; translate insights to actions.
🎁 Benefits
- Shared Success: Stock equity to share in company growth.
- Health & Wellness: 100% employer-paid medical, dental, and vision for dependents.
- Financial Well-being: 401(k), life and disability insurance, tax-advantaged accounts.
- Family Support: Paid parental leave after six months; Kindbody fertility benefits.
- Growth & Development: Stipends for professional/personal development and internal learning opportunities.
- Time Off & Flexibility: Flexible vacation policy and paid holidays.
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