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 tasksDevelop reliable scoring metrics that prevent manipulationIdentify key RL environments for developmentDesign robust performance measurement systemsOwn and implement projects from conception to deploymentRequirements:Expertise in low-level systems engineering (C/C++/CUDA)Proven record in Python for production-level codingHardware optimization understanding and applicationExperience with kernel development and profilingAbility to independently manage complete project cyclesBring your skills in kernel design to help revolutionize machine learning at Preference Model.#J-18808-Ljbffr

📌 Reinforcement Learning Engineer At Preference Model (Toronto)
🏢 Preference Model
📍 Toronto