Principal Applied Scientist, AAIS

What you'll do

• Set strategy for organizational knowledge representation, temporal reasoning, retrieval, and proactive AI behavior.
• Lead evaluation and data-generation approaches, and guide learning loops and model-efficiency decisions.
• Mentor scientists, prototype solutions, and drive scientific work toward production.

What they ask for

• PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field; or a Master's degree with 8+ years of applied science experience.
• 10+ years building and shipping machine learning or AI systems used in production.
• Expertise in large language models and at least two of: information retrieval, knowledge representation and graphs, reinforcement learning, agentic system design, or evaluation methodology for generative systems.
• Experience setting scientific and technical direction for scientists; proficiency in Python; and evidence of original scientific contribution through publications, patents, or equivalent.

Nice to have

• Experience with agentic systems, evaluation frameworks, synthetic data, memory or personalization, temporal knowledge representation, or knowledge graphs.
• Experience launching products under ambiguity or reducing inference costs through model distillation or domain-specific tuning.

Pay

198,900.00 - 269,000.00 USD annually (base salary)

Tools

Python, large language models

AI focus

Develops AI that represents and retrieves organizational knowledge, reasons over changes over time, acts proactively, learns from feedback, and supports team productivity.