Senior Applied Scientist
Core
Building foundational AI technologies for agentic systems, specifically focusing on model post-training (SFT, DPO, RFT/GRPO) and reward modeling to enable self-evolving agents in home shopping.
Role type
Senior Applied Scientist (LLM Post-Training & Reward Modeling)
Builds
Production-ready agentic AI systems for home shopping search, guidance, and offer strategy.
Domain
Generative AI, Reinforcement Learning, Home Shopping Technology
Deliverable
production ML models
Required skills
LLM post-training (SFT, DPO, RFT/GRPO), Reward model development, Generative AI (Transformers, RL, Preference learning), Python, PyTorch or TensorFlow, GPU infrastructure training, Technical leadership
Preferred skills
PhD in CS/ML, Agentic AI evaluation (LLM-as-a-Judge, multi-turn trace analysis), Published work in RLHF/RLAIF, GPU training platforms (Databricks, Fireworks)
Responsibilities
Own LLM post-training pipelines end-to-end, Build and train multi-category reward models, Design on-policy and online assessment frameworks, Translate offline evaluation rubrics into generalizable reward functions, Provide technical leadership and mentorship to scientists and MLEs
Seniority
Senior, hands-on IC with technical leadership
