Engineers optimize ML systems for performance at scale, focusing on GPU utilization, inference engines, and container runtime to boost throughput and reduce latency for language and diffusion models. Requires 5+ years experience with PyTorch, CUDA, and performance debugging.
Own the training pipeline for search and agent models, building from product usage data through fine-tuning and evaluation to production deployment. Requires deep expertise in transformer fine-tuning, data curation, and training models for ranking, retrieval, and agent behavior.
Own the multi-stage ranking pipeline for web-scale search, balancing precision, recall, latency, and compute cost across retrieval, first-pass ranking, and neural reranking.
Staff engineer builds AI-driven platform infrastructure for SDLC transformation, owns end-to-end experiments using AI agents like Claude, and ensures high-velocity code delivery with strong abstractions and real-world grounding. Requires staff-level architecture experience and AI-native workflows.
Develop voice AI models for natural, low-latency spoken interactions on the Grok team. Handle data pipelines, model training with JAX/PyTorch, evaluations, and product integrations. Requires Python expertise, large-scale data processing, and distributed systems experience.