About the job
We're looking for a senior scientist to lead the research direction for a system that gives AI persistent, compounding memory. This is a new problem space — not recommendation, not search, not summarization, though it draws from all three. The right scientist will define what this field becomes. You'll own the scientific roadmap, run a research agenda with real-world deployment targets, and mentor junior scientists. The team is forming now. Your first week will involve scoping experiments, not reading onboarding docs.
Responsibilities
Define the scientific roadmap for knowledge acquisition, representation, and retrieval at organizational scale.
Lead research on how AI systems should learn from experience — what to capture, how to generalize, when to forget.
Design evaluation frameworks for a system where "quality" means something new — right knowledge, right context, right confidence level.
Own end-to-end research from problem formulation through production impact measurement.
Mentor Applied Scientists and establish scientific standards for a new team.
Partner with engineering leadership to translate research into architecture decisions that shape the product.
Drive technical decisions on model architecture, training methodology, and evaluation frameworks, balancing scientific rigor with business impact.
Publish at top-tier venues and advance the state of the art in applied knowledge systems.
Qualifications
Minimum
4+ years of applied research experience
3+ years of building machine learning models for business application experience
PhD, or Master's degree and 6+ years of applied research experience
Experience programming in Java, C++, Python or related language
Experience with neural deep learning methods and machine learning
Preferred
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.