Principal Software Engineer – Large-Scale LLM Memory and Storage Systems

🕒 il y a 5 mois

🏄 California, Massachusetts, +1 états de plus – Distant

🏄 California – Remote 🍂 Massachusetts – Remote ☕ Washington – Remote

💵 $272 000 - $425 500 / an

⏰ Temps Plein

🔴 Expert

🧑‍💻 Développeur Full-Stack

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

Postuler Maintenant

Trouver des Emplois à Distance Similaires

Recevoir des emails pour des emplois à distance

📊 Vérifiez votre score de CV pour ce poste

Améliorez vos chances d'obtenir un entretien en vérifiant votre score de CV avant de postuler.

Télécharger le CV

NVIDIA

Site WebLinkedInTous les Emplois

10 000+ employés

Fondée en 1993

🤖 Intelligence artificielle

🎮 Jeux vidéo

Artificial Intelligence • Gaming • Automotive

NVIDIA est une entreprise technologique de premier plan, spécialisée dans le calcul accéléré et l’intelligence artificielle (IA). NVIDIA est à l’avant‑garde des avancées en GPU (processeurs graphiques), cloud computing, centres de données et réalité virtuelle, avec un accent particulier sur les secteurs du gaming, de l’automobile, de la santé et de la robotique. Ses innovations, telles que NVIDIA Omniverse, transforment les processus numériques traditionnels en permettant des simulations haute fidélité et des tâches de rendu de pointe. Ses applications couvrent de nombreux secteurs, des véhicules autonomes avec NVIDIA DRIVE aux solutions de santé avec NVIDIA Clara, ainsi que des analyses et workflows pilotés par l’IA.

Description

• Design and evolve a unified memory layer that spans GPU memory, pinned host memory, RDMA-accessible memory, SSD tiers, and remote file/object/cloud storage to support large-scale LLM inference • Architect and implement deep integrations with leading LLM serving engines (such as vLLM, SGLang, TensorRT-LLM), with a focus on KV-cache offload, reuse, and remote sharing across heterogeneous and disaggregated clusters • Co-design interfaces and protocols that enable disaggregated prefill, peer-to-peer KV-cache sharing, and multi-tier KV-cache storage (GPU, CPU, local disk, and remote memory) for high-throughput, low-latency inference • Partner closely with GPU architecture, networking, and platform teams to exploit GPUDirect, RDMA, NVLink, and similar technologies for low-latency KV-cache access and sharing across heterogeneous accelerators and memory pools • Mentor senior and junior engineers, set technical direction for memory and storage subsystems, and represent the team in internal reviews and external forums (open source, conferences, and customer-facing technical deep dives)

🎯 Exigences

• Masters or PhD or equivalent experience • 15+ years of experience building large-scale distributed systems, high-performance storage, or ML systems infrastructure in C/C++ and Python, with a track record of delivering production services • Deep understanding of memory hierarchies (GPU HBM, host DRAM, SSD, and remote/object storage) and experience designing systems that span multiple tiers for performance and cost efficiency • Distributed caching or key-value systems, especially designs optimized for low latency and high concurrency • Hands-on experience with networked I/O and RDMA/NVMe-oF/NVLink-style technologies, and familiarity with concepts like disaggregated and aggregated deployments for AI clusters • Strong skills in profiling and optimizing systems across CPU, GPU, memory, and network, using metrics to drive architectural decisions and validate improvements in TTFT and throughput • Excellent communication skills and prior experience leading cross-functional efforts with research, product, and customer teams.

🏖️ Avantages

• Equity • Benefits