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 up-to-date 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.

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