Senior Deep Learning Software Infrastructure Engineer

Job not on LinkedIn

🔥 6 hours ago

🏄 California – Remote

🏄 California – Remote

💵 $224k - $431.3k / year

⏰ Full Time

🟠 Senior

👷 Infrastructure Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Craft, scale, and harden deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters • Improve efficiency throughout the training stack, including data loaders, distributed training, scheduling, and performance monitoring • Build robust training pipelines and libraries for massive video datasets and rapid experimentation • Collaborate with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability • Own core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems • Partner with leadership to scale infrastructure with growing GPU capacity and dataset size while maintaining developer efficiency and stability

🎯 Requirements

• BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent experience • 12+ years of professional experience building and scaling high-performance distributed systems, ideally in ML, HPC, or large-scale data infrastructure • Extensive knowledge of deep learning frameworks, with PyTorch preferred • Knowledge of large-scale training, including DDP/FSDP, NCCL, tensor parallelism, and pipeline parallelism • Experience with performance profiling • Strong systems background in datacenter networking, including RoCE and IB • Experience with parallel filesystems, including Lustre • Knowledge of storage systems and schedulers such as Slurm and Kubernetes • Proficiency in Python and experience writing production-grade libraries, orchestration layers, and automation tools • Ability to work with ML researchers, infrastructure engineers, and product leads and translate requirements into robust systems • Experience scaling GPU training clusters with more than 1,000 GPUs • Expertise in fault resilience and high availability, including elastic training and large-scale observability • Hands-on technical leadership and ability to establish guidelines for ML systems engineering

🏖️ Benefits

• Equity • Benefits