Senior Solutions Architect, DL Training

NVIDIA • Seattle, WA

1d

About The Position

NVIDIA is seeking a Senior Solutions Architect to help customers build, scale, and optimize AI training solutions using NVIDIA GPU hardware and software on cloud computing platforms. The role focuses on foundation model pre-training and post-training, especially reinforcement learning. The Solutions Architect serves as a trusted technical advisor, working directly with developers, researchers, and infrastructure engineers at strategic technology customers and partnering with NVIDIA business, product, and engineering teams to inform product strategy. The ideal candidate has hands-on technical depth and can translate customer needs into scalable solutions using NVIDIA’s accelerated computing platform.

The role works with Cloud Service Providers to design, build, and demonstrate scalable AI training solutions. It leads technical engagements from architecture and proofs of concept through production adoption, develops and optimizes end-to-end training workflows, builds customer-specific proofs of concept, and helps diagnose performance bottlenecks. The position also involves partnering with sales and developer relations teams, delivering technical presentations and workshops, maintaining customer relationships, and collaborating with NVIDIA engineering and product teams. Occasional travel is required for on-site customer engagements and industry events.

The expected base salary is $152,000–$241,500 for Level 3 or $184,000–$287,500 for Level 4. The position is eligible for equity and benefits. Applications will be accepted at least until November 9, 2026. This posting is for an existing vacancy.

Requirements

• Bachelor’s, master’s, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience.
• At least 5 years of relevant professional experience in solutions architecture, software engineering, ML engineering, research engineering, or related technical roles, including at least 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.
• Track record 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.

Nice To Haves

• 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.

Responsibilities

• 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 progress 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 and demonstrate 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 to 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.
• Occasionally travel for on-site customer engagements and industry events.

Benefits

• Equity and benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior