Machine Learning Researcher Springtail

You will research and develop architectures for data-efficient model induction, including bootstrapped program synthesis. You will hypothesize, test, and refine approaches to improve generalization and attention mechanisms, devise controlled datasets, investigate runtime inference and constrained optimization, and contribute to a documented, instrumented codebase.

Responsibilities

• Hypothesize, test, and refine approaches to improve generalization performance of architectural elements including attention
• Devise controlled datasets to study learning order and learned representations
• Investigate runtime inference in gradient-trained networks using statistical-learning and constrained-optimization approaches
• Contribute to a well-documented, well-instrumented, performant codebase

Requirements

• Master's degree or equivalent in machine learning, mathematics, or an equivalent field
• Fluency with PyTorch
• Familiarity with JAX, CUDA, Triton, or their open-source ecosystems
• Demonstrated ability to conduct fundamental research
• Demonstrated ability to work in teams