Design and evaluate information access + reasoning strategies across RAG, agents, and classic ML
Prototype GenAI workflows that map and reason over compliance objects
Explore ML + probabilistic approaches where GenAI is not the best fit
Build and maintain evaluation frameworks
Implement and tune ranking/reranking systems
Run experiments to validate hypotheses and quantify improvements before production rollout
Debug failure modes and build error taxonomies across retrieval, reasoning, and generation
Collaborate with AI and Software Engineers to hand off validated approaches for productionization
Stay current on applied research in RAG, agents, LLM evaluation, and relevance modeling

6+ years of experience in applied research, data science, or ML with a focus on NLP, information retrieval, or knowledge systems
2+ years of hands-on experience building or contributing to production AI/ML systems
Strong foundation in information retrieval: dense and sparse retrieval, embedding models, search relevance
Experience with RAG systems: chunking strategies, vector databases, retrieval optimization
Proficiency in evaluation methodology: metrics design, golden dataset creation, A/B testing, statistical significance
Strong Python skills and comfort with notebook-driven research workflows
Experience communicating research findings to engineering teams
Bonus: Experience with compliance, legal, or document-heavy domains
Bonus: Publications or contributions in IR, NLP, or RAG evaluation

Up to 100% employer-paid premiums for medical, dental, and vision coverage for employees and their dependents
Comprehensive wellness benefits and healthcare concierge services
401(k) plan
Company-paid life and disability insurance
Tax-advantaged spending accounts
Paid Parental Leave policy
Access to Kindbody fertility and family-building benefits
Generous annual stipends for professional and personal development
Flexible vacation policy, paid holidays

Senior Applied Research Engineer

Key skills