Principal Applied Scientist, AAIS
Core
Define scientific strategy for organizational knowledge representation, temporal reasoning, and agentic behavior to make teams measurably faster.
Role type
Principal Applied Scientist (Science Leadership)
Builds
Multi-component agentic systems with durable, accurate team-level understanding
Domain
Generative AI, Knowledge Graphs, Temporal Reasoning, Agentic Systems
Deliverable
production ML models
Required skills
Large language models, information retrieval, knowledge representation, graph structures, temporal reasoning, agentic system design, evaluation methodology, synthetic data generation, model distillation
Preferred skills
RL-based post-training, environment simulation, memory/personalization systems, entity resolution, product launch under ambiguity
Technologies
Python, LLMs, Knowledge Graphs, Synthetic Data Pipelines
Responsibilities
Set scientific direction for knowledge extraction and retrieval; advance temporal reasoning and provenance tracking; define proactive behavior thresholds; build evaluation frameworks for agentic systems; design synthetic data pipelines; manage the learning loop from feedback to observation; optimize model efficiency and cost; mentor scientists and review designs.
Seniority
Principal, hands-on IC with team leadership