Drive innovation as a Reinforcement Learning Engineer with Preference Model. This role focuses on creating high-quality RL environments targeting real-world application complexities.

As part of the Low Level/Kernels Capabilities team, you’ll merge your engineering and research skills to build RL tasks that push the boundaries of ML. Your work will involve designing and scoring low-level kernels while ensuring robustness against unintentional model exploits. This unique setting presents challenges in hardware optimization and task design.

Key Responsibilities:
• Create kernel-focused RL environments for various tasks
• Develop reliable scoring metrics that prevent manipulation
• Identify key RL environments for development




• Design robust performance measurement systems
• Own and implement projects from conception to deployment

Requirements:
• Expertise in low-level systems engineering (C/C++/CUDA)
• Proven record in Python for production-level coding
• Hardware optimization understanding and application
• Experience with kernel development and profiling
• Ability to independently manage complete project cycles

Bring your skills in kernel design to help revolutionize machine learning at Preference Model.
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📌 Reinforcement Learning Engineer at Preference Model (Ontario)
🏢 Preference Model
📍 Ontario