Senior Applied Scientist

What you'll do

• Own LLM post-training pipelines including SFT, DPO, and reinforcement fine-tuning (RFT/GRPO) on GPU infrastructure.
• Build and train multi-category reward models (PRMs) that emit scalar signals from contrastive preference pairs.
• Design on-policy and online assessment systems to score live agent trajectories using LLM-as-a-Judge frameworks.
• Translate offline evaluation rubrics into generalizable reward functions for production traces.
• Translate state-of-the-art AI research in LLMs and reinforcement learning into production impact.
• Provide technical leadership and mentorship to scientists and machine learning engineers.
• Solve complex, ambiguous problems end-to-end across product, engineering, and data teams.

What we're looking for

• Master's degree or higher in Computer Science or a related field.
• Hands-on experience with LLM post-training and RL fine-tuning, including SFT, DPO, RFT, and GRPO.
• Experience running end-to-end training runs on GPU infrastructure.
• Exposure to reward model development.
• Strong knowledge of generative AI, foundation models, transformers, reinforcement learning, and preference learning.
• Ability to independently scope and solve ambiguous problems while providing technical leadership.
• Strong programming skills in Python and experience with ML frameworks like PyTorch or TensorFlow.
• PhD in Computer Science, Machine Learning, or a related field (preferred); published work in post-training or RLHF (preferred).