Senior Solutions Architect, Robotics Foundation Model Training - United States
$152,000 - $287,500 USD yearly
Originally posted 27 August 2026 by the employer.
About the role
This role focuses on developing and adopting Physical AI, encompassing data generation, large-scale multimodal model training, robotics simulation, and deployment.
What you'll do
- Architect and optimize end-to-end training workflows for robotics foundation models (e.g., World Models, VLAs, WAMs).
- Build proof-of-concepts, reference architectures, and agentic workflows for NVIDIA's Robotics Open model platforms (e.g., Cosmos, GR00T).
- Scale pre-training, fine-tuning, and reinforcement learning workloads across multi-GPU and multi-node systems.
- Identify and eliminate data pipeline bottlenecks across storage, networking, preprocessing, and data loading for multimodal datasets.
- Provide feedback to NVIDIA product and engineering teams to shape future Physical AI platforms.
What you'll need
- 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).
Skills: Robotics Foundation Model Training, Physical AI, large-scale multimodal model training, multi-GPU and multi-node systems, distributed training techniques
This role has been open 26 days — well below the 68-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
514
|
Median days open
68 d
|
Median salary
$232k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | NVIDIA | All employers we track in this specialty (514 roles · 75 employers) |
|---|---|---|
| Open roles in this specialty | 87 | 514 |
| Open roles in the wider Software, Firmware & Systems family | 1001 | 5948 · 143 employers |
| Median days open | 69 d | 68 d (+1 d vs this employer) |
| Median salary (USD postings) | — | $232k |
Skills observed across this category: Robotics Foundation Model Training, Physical AI, large-scale multimodal model training, multi-GPU and multi-node systems, distributed training techniques
Who's hiring in this category
- Qualcomm · 106 open roles · median 101 d
- NVIDIA (this employer) · 84 open roles · median 63 d
- AMD · 46 open roles · median 61 d
- Micron Technology · 33 open roles · median 43 d
- Mobileye · 21 open roles · median 57 d
- Analog Devices · 14 open roles · median 26 d
How we counted: 514 open AI/ML Hardware Engineering (AI ML Hardware) roles from 75 employers tracked in the SemiconductorJobs index, counted 23 Sept 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.