Senior Solutions Architect, Robotics Foundation Model Training

NVIDIA

Solutions Architect scaling NVIDIA robotics foundation-model training for Physical AI. Architecting distributed multimodal workloads and accelerating Cosmos and GR00T adoption.

Posted 8/27/2026full-timeRemote • California • 🇺🇸 United StatesSenior💰 $152,000 - $287,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and optimizing end-to-end training workflows for robotics foundation models, with a strong focus on distributed training techniques and multimodal training frameworks. Proven ability to collaborate effectively across research, engineering, and product teams to influence AI adoption and product direction.

Highest-signal resume keywords

Deep LearningDistributed ComputingMultimodal Training FrameworksReinforcement LearningData Pipeline Optimization

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Applicant Tracking System Keywords

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Hard Skills

Foundation Model TrainingMulti-GPU SystemsModel OptimizationData/Model/Pipeline ParallelismCheckpointingPre-trainingSupervised Fine-tuningEvaluationBatchingTokenization

Soft Skills

Strong Communication SkillsCollaboration

Tools & Technologies

PyTorchNVIDIA NeMoJAXHugging Face TransformersNsight SystemsNsight ComputePyTorch ProfilerCosmosGR00TIsaac Sim

Industry Keywords

RoboticsAI Model LifecycleHigh-Throughput Data PipelinesSynthetic Data GenerationAgentic Workflows

Tech Stack

Tools & technologies

Node.jsPyTorch

About the role

Key responsibilities & impact

Engage with researchers and ML engineers to architect and optimize end-to-end training workflows for robotics foundation models, including World Models, VLAs, and WAMs
Build proof-of-concepts, reference architectures, and agentic workflows to accelerate experimentation, benchmarking, and model improvement of NVIDIA’s robotics open model platforms, including Cosmos and GR00T
Scale pre-training, fine-tuning, and reinforcement learning workloads across multi-GPU and multi-node systems
Improve utilization, throughput, and memory efficiency
Identify and eliminate data pipeline bottlenecks across storage, networking, preprocessing, and data loading for multimodal datasets including video, sensor data, and trajectories
Collaborate with NVIDIA product and engineering teams to provide feedback shaping future Physical AI platforms
Work across research, engineering, and customer teams, influencing product direction and applied AI adoption

Requirements

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, 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, including data/model/pipeline parallelism, sharding, and checkpointing, 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 such as decoding, tokenization, and batching
Strong communication skills with the ability to effectively collaborate across Researchers, Engineers, and executives
Familiarity with NVIDIA AI and robotics platforms such as Cosmos, GR00T, NeMo, Isaac Sim, and Isaac Lab
Experience with robotics AI workloads, including reinforcement learning in simulation and synthetic data generation
Experience profiling and optimizing workloads using 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

Benefits

Comp & perks

Equity
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