Technical depth: This is a management-first role, but deep conceptual familiarity with at least one of our core research areas is highly valued:
Foundation models & LLMs: pre-training from scratch, scaling laws, training-optimization frameworks, and large GPU-cluster workflows.
Behavioral & sequential models: sequence models, recommendation systems, and large-scale representation learning for very large user bases.
Decisioning & optimization: causal inference, policy optimization, constrained optimization, or reinforcement learning.
Training & inference efficiency: model sparsification, quantization, distillation, or parallelism and partitioning design.