Description

In this role, you will drive innovation in reinforcement learning by developing cutting-edge algorithms and optimizing decision-making processes. Your day-to-day responsibilities will involve running experiments, curating training datasets, and collaborating with teams to enhance AI performance in real-world applications.

Requirements

• •Degree in Computer Science or related field
• •PhD in NLP, Machine Learning, or related field
• •Solid track record in AI R&D with good publications
• •Proven experience with large-scale reinforcement learning experiments
• •Experience with online RL techniques such as GRPO
• •Deep understanding of reinforcement learning algorithms
• •Expertise in enhancing policy stability, exploration, and sample efficiency
• •Strong expertise in PyTorch and relevant RL frameworks
• •Practical experience in developing RL pipelines
• •Demonstrated ability to apply empirical research to RL challenges
• •Proficiency in designing robust evaluation frameworks

Tasks

• •Develop and implement advanced reinforcement learning algorithms
• •Establish performance targets for reward maximization and policy stability
• •Build, run, and monitor controlled reinforcement learning experiments
• •Track key performance indicators and document results
• •Identify and curate high-quality simulation environments and training datasets
• •Set measurable criteria to enhance learning processes
• •Debug and optimize the reinforcement learning pipeline
• •Analyze computational efficiency and learning performance metrics
• •Address issues like reward signal noise and policy divergence
• •Collaborate with cross-functional teams to integrate reinforcement learning agents
• •Define success metrics for real-world performance improvements
• •Ensure continuous monitoring and iterative refinements

Tools & Technologies