Senior Solutions Architect, DL Training

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🔥 0 minutes ago

🏄 California, Washington – Remote

🏄 California – Remote ☕ Washington – Remote

⏰ Full Time

🟠 Senior

💻 Solutions Engineer

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👻 Ghost score 11%

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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

• Work with Cloud Service Providers to design, build, and demonstrate scalable AI training solutions using NVIDIA hardware and software • Lead technical engagements across foundation model pre-training and post-training, helping customers move from architecture and proofs of concept to production adoption • Develop and optimize end-to-end training workflows spanning data preparation, distributed training, checkpointing, and evaluation, including reinforcement learning pipelines • Build customer-specific proofs of concept addressing critical technical and business needs • Help customers diagnose and resolve bottlenecks across GPU compute, memory, communication, networking, storage, and workload orchestration • Partner with Sales Account Managers and Developer Relations Managers to identify opportunities and drive adoption of NVIDIA platforms for AI training • Prepare and deliver technical presentations, workshops, and architecture reviews for researchers, engineers, technical leaders, and executives • Establish close technical relationships with customers, lead roadmap and architecture discussions, and collaborate with NVIDIA engineering and product teams to resolve issues and inform future capabilities

🎯 Requirements

• BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience • 5+ years of relevant professional experience in solutions architecture, software engineering, ML engineering, research engineering, or related technical roles • 3+ years of hands-on experience with large-scale AI systems or distributed training • Strong programming and debugging skills in Python • Hands-on experience developing and optimizing deep learning workloads using PyTorch • Practical experience with foundation model pre-training or post-training • Strong understanding of the broader model development lifecycle • Demonstrated ownership of an end-to-end training workflow, including data preparation, training configuration, execution, evaluation, and troubleshooting • Experience with multi-GPU and multi-node training, including distributed parallelism, mixed precision, memory optimization, and checkpointing • Track record of deploying GPU workloads in cloud computing environments such as AWS, GCP, or Azure • Knowledge of containers and workload orchestration • Ability to lead technical engagements, solve complex problems, and communicate technical ideas and tradeoffs clearly to technical and non-technical audiences • Hands-on experience spanning both pre-training and post-training of large language or multimodal models, including supervised fine-tuning and reinforcement learning • Experience with reinforcement learning from human feedback or verifiable rewards, including reward design, rollout generation, policy optimization, and training stability • Experience with NVIDIA NeMo Framework, Megatron Core, Megatron Bridge, PyTorch FSDP, DeepSpeed, or comparable distributed training technologies • System-level understanding of GPU platforms • Experience profiling and optimizing training workloads

🏖️ Benefits

• Equity • Benefits • Occasional travel for on-site customer engagements and industry events

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