ModalNew York, NYEngineers 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.
Machine Learning Engineer owning the full ML lifecycle for multimodal video datasets at Sieve. Fine-tune VLMs, build evaluation/QA pipelines with frontier models, design filtering systems over internet-scale data, and ship production improvements for top AI labs. Requires strong Python, PyTorch, and production ML experience.
Member of Technical Staff conducting hands-on LLM inference research at Modal. Own end-to-end bets on techniques like speculative decoding, quantization, KV-cache management, and disaggregation to improve cost per token and tail latency on production workloads. Requires strong LLM serving stack expertise and a track record shipping research or systems.
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.