Senior Solutions Architect, Robotics Foundation Model Training

NVIDIA
Santa Clara
Full timeUSD 152,000 - 287,500 annuallyFunction: Solutions Engineering & Sales EngineeringExperience: 5+ yearsEducation: mastersSkills: ["Communication","Collaboration","Problem solving","Architecting workflows"]

Build and optimize end-to-end training workflows for robotics foundation models, partnering with researchers and ML engineers from experimentation to production. Develop reference architectures and agentic workflows, and scale pre-training, fine-tuning, and reinforcement learning across multi-GPU/multi-node systems. Identify and remove multimodal data pipeline bottlenecks, improve training efficiency using profiling tools, and provide feedback that shapes NVIDIA Physical AI platforms.

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FursaFursa
NVIDIA
NVIDIA
3 days ago

Senior Solutions Architect, Robotics Foundation Model Training

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Source: Company careers pageValidated by: Fursa AI
Last checked: 4 hours agoStatus: Live

Job Summary

Build and optimize end-to-end training workflows for robotics foundation models, partnering with researchers and ML engineers from experimentation to production. Develop reference architectures and agentic workflows, and scale pre-training, fine-tuning, and reinforcement learning across multi-GPU/multi-node systems. Identify and remove multimodal data pipeline bottlenecks, improve training efficiency using profiling tools, and provide feedback that shapes NVIDIA Physical AI platforms.
Location: Santa Clara
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering

Key Responsibilities

  • •Architect and optimize end-to-end training workflows for robotics foundation models with ML engineers and researchers.
  • •Create proof-of-concepts, reference architectures, and agentic workflows to accelerate experimentation, benchmarking, and model improvement using NVIDIA robotics open model platforms.
  • •Scale pre-training, fine-tuning, and reinforcement learning workloads across multi-GPU and multi-node systems to improve utilization, throughput, and memory efficiency.
  • •Detect and eliminate data pipeline bottlenecks across storage, networking, preprocessing, and data loading for multimodal datasets.
  • •Collaborate with product and engineering teams to provide feedback shaping future Physical AI platforms.

Pay and Benefits

Salary: USD 152,000 - 287,500 annually
Equity and Bonus:Equity

Key Requirements

  • •MS, PhD, or equivalent experience in Computer Science, AI, Electrical/Computer Engineering, Robotics, or a related field.
  • •5+ years in deep learning, distributed computing, or large-scale model training (industry or research).
  • •Hands-on experience training and/or optimizing multimodal or foundation models (e.g., VLMs, VLAs, World Models), ideally in robotics settings.
  • •Experience across the AI model lifecycle: pre-training, supervised fine-tuning, RL or post-training methods, evaluation, and optimization.
  • •Strong distributed training expertise (e.g., data/model/pipeline parallelism, sharding, checkpointing) on multi-GPU or multi-node systems.
Experience:5+ yearsDeep learningDistributed computingLarge-scale model trainingRoboticsFoundation modelsMultimodalReinforcement learning
Education:Master's
Skills:CommunicationCollaborationProblem solvingArchitecting workflows
Tech Stack:PyTorchNVIDIA NeMoJAXHugging Face TransformersNsight SystemsNsight ComputePyTorch ProfilerCosmosGR00TIsaac SimIsaac Lab

Company Brief

NVIDIA
Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.
Industry: Electronics Manufacturing
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1993
Glassdoor
Glassdoor: 4.3
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