Tabiya is looking to bridge the gap from a research prototype to a demonstrable MVP for their AI-powered matching and analytics engine, Horizon, to make labor markets more efficient, equitable, and inclusive.
Requirements
- Strong Python development skills in writing production-grade code, not just notebooks
- Experience evaluating and making architectural decisions about ML systems
- NLP and text processing experience
- Experience designing and conducting ML model evaluations (match quality, bias/fairness)
- Cloud deployment experience (preferably GCP)
- Practical understanding of ML explainability
- Experience refactoring research code or building MVPs from prototypes
Responsibilities
- Defining core functionalities and specifications needed to bridge prototype to MVP, in close coordination with Tabiya’s management and research teams
- Designing and implementing the matching algorithm (refactored from prototype or rebuilt) as clean, modular, well-documented Python code
- Building analytics capabilities that surface insights from aggregated data on jobseeker and opportunity side
- Testing with real partner data from 3-4 organizations to validate matching quality and identify improvements
- Building a simple demonstration interface that allows non-technical users to interact with the matching engine
- Deploying to a cloud platform with a straightforward, maintainable architecture (not necessarily production-grade infrastructure)
- Creating clear documentation for future technical teams to understand, modify, and scale Horizon as a marketable product
Other
- 4-6 years of experience in applied ML engineering or data science
- Self-directed, makes good technical judgments with incomplete information
- Strong documentation and communication skills
- Familiarity with employment/labor market domains or skills taxonomies
- Comfort working with messy, real-world data from diverse sources
