Be a part of Preference Model's innovative 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 efficient 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 processes
Requirements:
• Proven experience with LLM post-training pipelines
• Proficient in Python with PyTorch or JAX
• Knowledge of contemporary RL frameworks
• Experience in scalable ML infrastructure
Utilize your unique skill set at Preference Model and contribute to transformative AI research.
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📌 Automated ML Research Engineer Role (Toronto)
🏢 Preference Model
📍 Toronto