Pave is building the industry's leading compensation platform to help companies make better compensation decisions by combining real-time compensation data with AI and machine learning. The current industry standard relies on flawed statistics and spreadsheets, and Pave aims to provide a more robust, data-driven solution.
Requirements
- 5+ years of experience building and deploying ML models in production environments
- Strong foundation in machine learning, statistics, and deep learning fundamentals
- Expertise in Python and modern ML frameworks (PyTorch, TensorFlow, or similar)
- Experience with large-scale data processing and ML model optimization
- Experience with MLOps practices and tools (model versioning, monitoring, and deployment)
- Strong software engineering practices and experience with production systems
- Expert-level SQL skills with experience writing complex queries and optimizing query performance
Responsibilities
- Architect and implement scalable ML systems for modeling compensation within a single company and across the market as a whole
- Collaborate with product and engineering teams to identify additional opportunities to leverage ML-driven solutions
- Help evolve the technical direction of ML initiatives across the company
- Drive millions of dollars of revenue growth
Other
- Ability to navigate (and bring structure to) ambiguity; ability to bring a project from 0 to 1, or scale a project from 1 to 100
