Platform Data Science team researches, prototypes and ships machine learning features for MongoDB's core server, query engine and Atlas
Work alongside engineers from Server Query, Atlas Clusters, and Release Quality
Responsibilities
Partner with Server Query, Atlas Clusters, Release Quality and other engineers to embed algorithmic rigor and optimization into resource scaling, release-safety and monitoring systems
Deliver production-ready, thoroughly tested statistical and ML algorithms with well-identified limitations
Own the full feedback loop: instrument model architecture with metrics, create dashboards, collect feedback, and iterate
Deliver thoughtful code reviews and act as a core contributor to internal packages, tooling, and team processes
Requirements
5+ years of hands-on machine learning model development, working directly with technical stakeholders
Expertise working autonomously across the entire machine learning development lifecycle, including prototyping, simulation, tuning and iterating in deployment environments
Object-oriented approach to designing scalable and readable Python codebase; experience with architecture design of machine learning systems
Codebase primarily in Python; AI used for developer productivity
Effective at communicating technical ML concepts to non-ML-expert audiences
Master's degree or equivalent experience in a quantitative/computational discipline (computer science, applied mathematics, statistics, physics, operations research, etc.)