Research Engineer (Machine Learning)
Core
Develop high-performance software and algorithms for Symbolic Regression (SR) to discover interpretable mathematical laws for explainable AI and AI for Science.
Role type
Research Engineer (Machine Learning)
Builds
Large-scale software pipelines for model searches and benchmarking tools
Domain
AI for Science / Explainable AI / Symbolic Regression
Deliverable
production ML models
Required skills
Python (NumPy, Pandas, Scikit-learn), C++, SymPy, Hyperparameter tuning, HPC cluster management, LaTeX
Preferred skills
Symbolic Regression libraries (PySR, gplearn), Mathematical theory (bounds, stability)
Technologies
Python, NumPy, Pandas, Scikit-learn, C++, SymPy, PySR, gplearn
Responsibilities
Build and manage pipelines for large-scale exhaustive model searches, Execute rigorous hyperparameter tuning and performance benchmarking, Assist in applying SR-informed models to real-world healthcare and scientific datasets, Curate and release large-scale research datasets to the global community
Seniority
Mid-level, hands-on IC
