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
Strong AI research record with leading conference publications
Large-scale multimodal reinforcement learning experimentation experience
Advanced PyTorch and contemporary 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.