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.
What we need to see
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.
Ways to stand out from the crowd
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!
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 9, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
