Principal Applied Scientist, Agentic AI
Core
Lead the design and deployment of reinforcement learning (RL) post-training systems to align large models with user value, safety, and business objectives in real products.
Role type
Principal Applied Scientist (RL Post-Training)
Builds
Production AI models and post-training pipelines for real estate platform experiences
Domain
Real Estate / Applied Machine Learning / Reinforcement Learning
Deliverable
production ML models
Required skills
Reinforcement learning, Post-training techniques (SFT, DPO, RLHF, RLAIF), Multi-objective optimization, Reward modeling, Transformer-based models, Evaluation frameworks, Technical leadership
Preferred skills
Experience in high-stakes verticals (finance, healthcare), Thought leadership, External representation
Technologies
LLMs, Multimodal models, Vector search, Orchestration frameworks
Responsibilities
Lead technical direction and strategy for RL post-training; Design and implement post-training pipelines; Develop reward models and objective formulations; Translate logs and signals into training data; Partner with teams to improve training efficiency and robustness; Mentor scientists and engineers.
Seniority
Principal, hands-on IC with broad influence
