Robotics Foundation Model Trainer & Systems Architect

NVIDIA; Santa Clara (CA); On-site; USD 152,000 - 288,000; Full time

Job summary

NVIDIA is seeking an Applied Engineer to lead hands‑on efforts in training robotics foundation models at scale. You will work with researchers and ML engineers to optimize end‑to‑end workflows, build reference architectures, and scale workloads across multi‑GPU systems.

Collaboration spans research, engineering and customers to push the envelope of Physical AI. The role demands advanced degrees or equivalent experience in CS/AI/EE/Robotics, 5+ years in deep learning or large‑scale training, and

Qualifications

• MS/PhD or equivalent experience in CS/AI/EE/Robotics or related field.
• 5+ years of industry or research experience in deep learning or large‑scale model training.
• Hands‑on experience training or optimizing multimodal or foundation models, ideally in robotics.
• Experience across the AI model lifecycle: pre-training, fine‑tuning, RL/post‑training, evaluation.
• Strong expertise in distributed training techniques on multi‑GPU or multi‑node systems.
• Experience with multimodal training frameworks (PyTorch, NeMo, JAX, Transformers).
• Experience building high‑throughput data pipelines for large‑scale training.

Responsibilities

• Architect and optimize end‑to‑end training workflows for robotics foundation models.
• Build proofs‑of‑concepts and reference architectures to accelerate experimentation.
• Scale pre‑training, fine‑tuning, and RL workloads on multi‑GPU/multi‑node systems.
• Identify and fix data pipeline bottlenecks across storage, networking, and preprocessing.
• Collaborate with product and engineering teams to inform platform direction.

Skills

Deep learning

Distributed computing

Multimodal models

PyTorch

NVIDIA NeMo

JAX

Hugging Face Transformers

Data pipelines

Education

MS/PhD or equivalent

Tools

Nsight Systems

Nsight Compute

PyTorch Profiler

Job description

NVIDIA is seeking an Applied Engineer to lead hands‑on efforts in training robotics foundation models at scale. You will work with researchers and ML engineers to optimize end‑to‑end workflows, build reference architectures, and scale workloads across multi‑GPU systems.

Collaboration spans research, engineering and customers to push the envelope of Physical AI. The role demands advanced degrees or equivalent experience in CS/AI/EE/Robotics, 5+ years in deep learning or large‑scale training, and