Advance agentic reinforcement learning research from Canada with PyTorch and multimodal AI expertise in a fully remote setting. Build intelligent systems for complex challenges.This AI Research Engineer role focuses on next-generation intelligent systems and adaptive behavior. You will develop reinforcement learning algorithms for multimodal models, scalable training infrastructure, reward strategies, and evaluation frameworks.

Research spans images, audio, text, diffusion-based generative models, autoregressive architectures, and unified multimodal systems.Key Responsibilities:

Develop reinforcement learning algorithms for multimodal AI modelsDesign distributed infrastructure for scalable reinforcement learning trainingOptimize rewards, stability, alignment,



and reward-hacking preventionCreate simulation environments and multimodal training datasetsConduct experiments, benchmarks, evaluations, and research publicationsRequirements:

Master's degree in Computer Science or related disciplineStrong AI research record with leading conference publicationsLarge-scale multimodal reinforcement learning experimentation experienceAdvanced PyTorch and up-to-date deep learning framework proficiencyExperience building end-to-end reinforcement learning pipelinesApply agentic reinforcement learning, multimodal modeling, PyTorch, and rigorous experimentation to improve autonomous AI systems remotely.

📌 Agentic Reinforcement Learning Engineer (Winnipeg)
🏢 Jobgether
📍 Winnipeg