Research Scientist, Frontier, Zurich
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El puesto
Design and validate novel post-training pipelines (SFT, RLHF, RLAIF) for frontier-class models where no teacher model exists.
Lead research into next-generation Reward Models, exploring architectures, reducing reward hacking, and improving data signal-to-noise ratios.
Develop methods to enhance internal reasoning and Chain-of-Thought capabilities, emphasizing correctness and multi-step problem solving.
Revamp RL paradigms and prompts to maximize performance while maintaining alignment and safety.
Create robust mechanisms to transform user signals into training data, building a flywheel that scales without regression or bias.
Collaborate across teams to apply these recipes to various sizes and modalities, including audio and multimodal tasks.
Lead research into next-generation Reward Models, exploring architectures, reducing reward hacking, and improving data signal-to-noise ratios.
Develop methods to enhance internal reasoning and Chain-of-Thought capabilities, emphasizing correctness and multi-step problem solving.
Revamp RL paradigms and prompts to maximize performance while maintaining alignment and safety.
Create robust mechanisms to transform user signals into training data, building a flywheel that scales without regression or bias.
Collaborate across teams to apply these recipes to various sizes and modalities, including audio and multimodal tasks.
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