Skill
Hyperparameter Tuning
Data Science, Analytics and AI/ML
Hyperparameter tuning is the process of finding the optimal configuration settings (such as learning rate, batch size, or number of layers) for a machine learning model that are not learned from data but set before training. Data scientists and machine learning engineers use techniques like grid search, random search, and Bayesian optimization to improve model performance and generalization. It's a critical step in the ML development lifecycle that can significantly affect accuracy and training efficiency.
Open roles requiring Hyperparameter Tuning (1)
Senior AI Software Engineer, Reinforcement Learning
Agility Robotics
Full-time · Fremont, CA · $187,000 – $292,000
This role develops and deploys reinforcement learning controllers for Agility's Digit humanoid robot, enabling it to move safely and manipulate objects in real warehouse and manufacturing environments. You'll design RL policies, integrate perception systems, build training infrastructure, and ship production policies to robots already operating with major customers.
Listed on Agility Robotics’s careers site · Apply there ↗
Roles that use Hyperparameter Tuning
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