Senior Solutions Architect, Robotics Foundation Model Training

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

We are building a team of innovators to help partners develop and adopt the next generation of Physical AI, spanning data generation, large-scale multimodal model training, robotics simulation and deployment!

We are looking for a hands‑on Applied Engineer with deep expertise in training foundation models at scale and a strong background in robotics. This role operates at the intersection of innovative AI research, accelerated computing and real-world applications, offering a unique opportunity to work directly with model builders to scale cutting‑edge Robotics Models from experimentation to production. Collaboration spans research, engineering, and customer teams, influencing both product direction and applied AI adoption. Come join us and help shape the future of robotics foundation model training!

What You’ll Be Doing:

• Engage with Researchers and ML engineers to architect and optimize end‑to‑end training workflows for robotics foundation models, like World Models, VLAs, WAMs.

• Build proof‑of‑concepts, reference architectures, and agentic workflows that accelerate experimentation, benchmarking, and model improvement of NVIDIA’s Robotics Open model platforms like Cosmos and GR00T.

• Scale pre‑training, fine‑tuning, and reinforcement learning workloads across multi‑GPU and multi‑node systems, improving utilization, throughput, and memory efficiency.

• Identify and eliminate data pipeline bottlenecks across storage, networking, preprocessing, and data loading for multimodal datasets (video, sensor data, trajectories).

• Collaborate with NVIDIA product and engineering teams to provide feedback that shapes future Physical AI platforms.

What We Need to See:

• MS, PhD, or equivalent experience in Computer Science, Artificial Intelligence, Electrical or Computer Engineering, Robotics, or a related field.

• 5+ years of industry or research experience in deep learning, distributed computing, or large‑scale model training.

• Hands‑on experience training or optimizing multimodal or foundation models (e.g., VLMs, VLAs, World Models), ideally in robotics settings.

• Experience across the AI model lifecycle, including pre‑training, supervised fine‑tuning, RL or other post‑training methods, evaluation, and model optimization.

• Strong expertise in distributed training techniques (data/model/pipeline parallelism, sharding, check‑pointing) on multi‑GPU or multi‑node systems.

• Expertise with multimodal training frameworks such as PyTorch, NVIDIA NeMo, JAX, or Hugging Face Transformers.

• Experience building or working with high‑throughput data pipelines for large‑scale training, including storage bandwidth, network throughput, and preprocessing (e.g., decoding, tokenization, batching)

• Strong communication skills with the ability to effectively collaborate across Researchers, Engineers and executives.

Ways to Stand Out From the Crowd:

• Familiarity with NVIDIA AI and robotics platforms (e.g., Cosmos, GR00T, NeMo, Isaac Sim, Isaac Lab)

• Experience with robotics AI workloads, including reinforcement learning in simulation and synthetic data generation.

• Experience profiling and optimizing workloads using tools such as Nsight Systems, Nsight Compute, or PyTorch Profiler

• Demonstrated impact improving training efficiency and scaling performance

• Experience building agentic workflows for automated experimentation, model evaluation, data analysis, or research acceleration

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until August 31, 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.