Investigate and implement strategies for handling conflicting, incomplete, or time-sensitive information.
Translate product requirements into ML constraints and possibilities.
Collaborate with engineers to validate feasibility and de-risk system architecture.
Focus on experimentation and implementation for our Python-based knowledge service.
Design, test and implement retrieval strategies, evaluate system trustworthiness, and help define how the service handles conflicting or outdated information.
Work closely with product and engineering teams to ensure our AI/ML foundations are technically strong, reliable, and aligned with product goals.
Requirements
8+ years of professional or research experience, with deep expertise in AI/ML research or applied machine learning.
Strong background in Python and experience with retrieval-augmented generation, LLMs, or knowledge graphs.
Demonstrated ability to design experiments, evaluate systems rigorously, and communicate findings.
Strong collaborator and communicator, able to bridge research and engineering.
Nice to Have Experience with Neo4j or other graph databases.