2 days ago
Base Salary
$224k - $357k/yr
Responsibilities
- Design and maintain large-scale distributed training systems for multimodal foundation models in robotics.
- Optimize GPU and cluster utilization for model training and fine-tuning on massive datasets.
- Implement scalable data loaders and preprocessors for multimodal video, text, and sensor data.
- Develop monitoring and debugging tools for reliable, high-performance training workflows on large GPU clusters.
- Collaborate with researchers to integrate new model architectures into scalable training pipelines.
Requirements
- Bachelor’s degree in Computer Science, Robotics, Engineering, or a related field.
- 10+ years of full-time industry experience in large-scale MLOps and AI infrastructure.
- Experience designing and optimizing distributed training systems with PyTorch, JAX, or TensorFlow.
- Deep understanding of GPU acceleration, CUDA programming, and cluster management tools such as Kubernetes.
- Strong Python programming skills and proficiency in a high-performance language such as C++.
- Strong experience with large-scale GPU clusters, HPC environments, and job scheduling or orchestration tools such as SLURM and Kubernetes.
- Master’s or PhD degree in a relevant field is preferred.
- Tech lead experience coordinating engineers and driving projects from conception to deployment is preferred.
- Experience building large-scale LLM and multimodal LLM training infrastructure is preferred.
- Contributions to open-source AI frameworks or research publications at conferences such as NeurIPS, ICRA, ICLR, or CoRL are preferred.
Benefits
- Base salary is listed as $224,000–$356,500 per year, with eligibility for equity and benefits.
- Applications will be accepted at least until November 3, 2026.
- This posting is for an existing vacancy.
Tech Stack
Categories
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.