Agentic Reinforcement Learning Engineer

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 modern 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.
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