1 day ago
Responsibilities
- Design, build, and demonstrate scalable AI training solutions using NVIDIA hardware and software on cloud platforms.
- Lead customer technical engagements from architecture and proofs of concept through production adoption for foundation model pre-training and post-training.
- Develop and optimize end-to-end training workflows covering data preparation, distributed training, checkpointing, evaluation, and reinforcement learning.
- Build customer-specific proofs of concept and improve training performance, reliability, and efficiency.
- Diagnose bottlenecks across GPU compute, memory, communication, networking, storage, and workload orchestration.
- Partner with sales, developer relations, product, and engineering teams to identify opportunities, resolve issues, and inform product strategy.
- Deliver technical presentations, workshops, architecture reviews, and roadmap discussions to technical and executive audiences.
- Establish technical relationships with strategic customers and cloud service providers.
Requirements
- BS, MS, 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.
- At least 3 years of hands-on experience with large-scale AI systems or distributed training.
- Strong Python programming and debugging skills and hands-on experience developing and optimizing deep learning workloads with PyTorch.
- Experience with foundation model pre-training or post-training and the broader model development lifecycle.
- Experience owning end-to-end training workflows, including data preparation, configuration, execution, evaluation, and troubleshooting.
- Experience with multi-GPU and multi-node training, distributed parallelism, mixed precision, memory optimization, and checkpointing.
- Experience deploying GPU workloads in AWS, GCP, or Azure cloud environments, with knowledge of containers and workload orchestration.
- Ability to lead technical engagements, solve complex problems, and communicate technical concepts and tradeoffs clearly.
- Preferred experience with large language or multimodal model pre-training and post-training, supervised fine-tuning, reinforcement learning, reward design, rollout generation, policy optimization, or training stability.
- Preferred experience with NVIDIA NeMo Framework, Megatron Core, Megatron Bridge, PyTorch FSDP, DeepSpeed, or comparable distributed training technologies.
- Preferred system-level GPU knowledge and experience profiling and optimizing training workloads for throughput, scaling efficiency, reliability, or cost.
Benefits
- Equity and benefits are provided.
- Conferencing tools are used extensively; occasional travel is required for on-site customer engagements and industry events.
- Applications are accepted at least until November 9, 2026.
Categories
Solutions Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.