Lead technical design, build, and demonstration of scalable AI training solutions on NVIDIA GPU hardware and software for enterprise customers.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
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 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.
Requirements
BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience.
Nice to have: reinforcement learning from human feedback
Senior Solutions Architect to design, build, and optimize AI training solutions using NVIDIA GPU hardware and software on cloud platforms.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
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 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.
Requirements
BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience.
