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 models Design distributed infrastructure for scalable reinforcement learning training Optimize rewards, stability, alignment,
and reward-hacking prevention Create simulation environments and multimodal training datasets Conduct experiments, benchmarks, evaluations, and research publications Requirements:
Master's degree in Computer Science or related discipline Solid AI research record with leading conference publications Large-scale multimodal reinforcement learning experimentation experience Advanced PyTorch and modern deep learning framework proficiency Experience building end-to-end reinforcement learning pipelines Apply agentic reinforcement learning, multimodal modeling, PyTorch, and rigorous experimentation to improve autonomous AI systems remotely.