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