Build and optimize ML training systems for production-scale language models using PyTorch and frameworks like Hugging Face. Requires 5+ years experience in high-performance code and training optimizations; onsite in NYC or SF.
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.
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.