Master's degree or above, with at least 3 years of hands-on experience in Reinforcement Learning (RL), Reinforcement Learning from Human Feedback (RLHF), or Reinforcement Learning with Verifiable Rewards (RLVR). Experience in Agent-related projects is preferred.
Master's degree or above, with at least 3 years of hands-on experience in Reinforcement Learning (RL), Reinforcement Learning from Human Feedback (RLHF), or Reinforcement Learning with Verifiable Rewards (RLVR). Experience in Agent-related projects is preferred.
Performs comprehensive functionality tests of new and updated applications to verify desired results are achieved. Evaluates results and coordinates or makes modifications to improve program performance, effectiveness, and maintainability.
Build the agentic-security surface. Partner on red-teaming and adversarial evaluation - tool-chain exploits, RBAC and permission-inheritance failures, multi-turn safety erosion - so we catch what a motivated adversary would.
Develop labeling systems that support dataset segmentation, coverage analysis, quality filtering, training-data mixtures, and model performance attribution.