Principal Applied Scientist

Job Summary

Driving complex user modeling initiatives, the full-time remote Principal Applied Scientist will build and train user behavior models, manage LLM post-training pipelines, and collaborate with cross-functional teams to translate advanced AI research into practical applications.

Key responsibilities

• Own LLM post-training pipelines including SFT, DPO, and reinforcement fine-tuning
• Build and train multi-category reward models to derive scalar signals from preference pairs
• Provide technical leadership and mentorship while translating AI research into production impact

Required qualifications

• PhD in Computer Science, Statistics, Economics, Machine Learning, or a related quantitative field with expertise in reinforcement learning or causal inference
• Hands-on experience modeling user behavior at scale, including sequence models and prediction of user intent
• Practical knowledge in causal inference or reinforcement learning applied to sequential decision problems
• Ability to independently scope and solve complex problems while providing technical leadership
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow