Senior Solutions Architect, DL Training

NVIDIA is building the world’s leading AI company, and we are looking for an experienced Senior Solutions Architect to help customers build, scale, and optimize AI training solutions using NVIDIA GPU hardware and software on cloud computing platforms. This role focuses on foundation model pre-training and post-training, with an emphasis on reinforcement learning.

As part of the Solutions Architecture team, you will work with some of the most exciting computing hardware and software technologies, including the latest advances in large-scale AI training. A Solutions Architect serves as a trusted technical advisor between NVIDIA and our customers. You will engage directly with developers, researchers, and infrastructure engineers at some of NVIDIA’s most strategic technology customers, while partnering with our business, product, and engineering teams to inform product strategy. The ideal candidate combines hands-on technical depth with the ability to translate customer needs into scalable solutions using NVIDIA’s accelerated computing platform. Join us in this exciting endeavor!

• →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 that address critical technical and business needs, demonstrating improvements in training performance, reliability, and efficiency.
• →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 audiences ranging from researchers and engineers to 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.

• 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, including 3+ years of hands-on experience with large-scale AI systems or distributed training.
• Strong programming and debugging skills in Python, with hands-on experience developing and optimizing deep learning workloads using PyTorch.
• Practical experience with foundation model pre-training or post-training, and a 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.
• Established track record of deploying GPU workloads in cloud computing environments such as AWS, GCP, or Azure, with 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 and experience profiling and optimizing training workloads, with measured improvements in throughput, scaling efficiency, reliability, or cost.
  

We make extensive use of conferencing tools, but occasional travel is required for on-site customer engagements and industry events. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative and autonomous, we want to hear from you!

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.