Be a part of Preference Model's creative team as an Automated ML Research Engineer. Focus on developing RL training environments to enhance model performance.In this unique role, you'll explore the capabilities of self-training models within complex RL environments. Your responsibilities will include training and evaluating models, optimizing infrastructure, and creating productive training methodologies. The role demands both engineering and research mindset to drive data-driven learning.Key Responsibilities:
Train and evaluate models on proprietary RL environments
Architect RL training infrastructure for various workloads
Create and test RL agent training environments
Optimize data loading and reward computation processesRequirements:
Proven experience with LLM post-training pipelines
Proficient in Python with PyTorch or JAX
Knowledge of contemporary RL frameworks
Experience in scalable ML infrastructureUtilize your unique skill set at Preference Model and contribute to transformative AI research.#J-18808-Ljbffr

📌 Automated Ml Research Engineer Role Toronto
🏢 Preference Model
📍 Toronto