Principal Applied Scientist (User Modeling)

What you'll do

• Own end-to-end LLM post-training pipelines on GPU infrastructure, including SFT, DPO, and reinforcement fine-tuning.
• Build reward models and design online assessment of agent trajectories, translating evaluation rubrics into production reward functions.
• Lead complex cross-team work and mentor scientists while bringing AI research into production.

What they ask for

• PhD in Computer Science, Statistics, Economics, Machine Learning, or a related quantitative field, with depth in reinforcement learning or causal inference.
• Hands-on experience modeling user behavior at scale with sequence models and predicting user intent or long-term outcomes.
• Practical depth in causal inference or reinforcement learning applied to sequential decision problems.
• Working proficiency in Python and ML frameworks such as PyTorch or TensorFlow.

Nice to have

• Background in economics or econometrics, such as demand modeling or consumer choice.
• Published work in RL, causal inference, survival analysis, or user modeling/simulation.

Pay

$195,600.00 - $312,400.00 annually (base salary); $185,800.00 - $296,800.00 annually (base salary)

Tools

SFT, DPO, RFT/GRPO, GPU, LLM-as-a-Judge, Python, PyTorch, TensorFlow

AI focus

Build user world models and agent-training, reward-modeling, and evaluation systems using sequence modeling, causal inference, reinforcement learning, and LLM post-training.