Deep Learning Solution Architect - RL and Post-training

NVIDIA
Beijing
Workplace: OnsiteFull timeFunction: Solutions Engineering & Sales EngineeringExperience: 5+ yearsEducation: mastersSkills: ["Independent work","Communication","Collaboration","Problem-solving","Exploratory experimentation"]

Drive and optimize reinforcement learning algorithms and infrastructure for large language and multimodal models, partnering across industry sales, developer relations, and product teams. Develop and enable prompt open-source model support within NVIDIA RL and post-training frameworks, and validate state-of-the-art RL methods for NVIDIA platform readiness. Maintain reusable toolchains and experiment workflows, and provide technical guidance to customers integrating NVIDIA RL into their AI pipelines.

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FursaFursa
NVIDIA
NVIDIA
6 hours ago

Deep Learning Solution Architect - RL and Post-training

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Source: Company careers pageValidated by: Fursa AI
Last checked: 6 hours agoStatus: Live

Job Summary

Drive and optimize reinforcement learning algorithms and infrastructure for large language and multimodal models, partnering across industry sales, developer relations, and product teams. Develop and enable prompt open-source model support within NVIDIA RL and post-training frameworks, and validate state-of-the-art RL methods for NVIDIA platform readiness. Maintain reusable toolchains and experiment workflows, and provide technical guidance to customers integrating NVIDIA RL into their AI pipelines.
Location: Beijing
Workplace: Onsite
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering
Seniority: Mid level

Key Responsibilities

  • •Develop and optimize reinforcement learning algorithms and infrastructure for large language and multimodal models.
  • •Develop and enable prompt open-source model support in NVIDIA RL and post-training frameworks.
  • •Collaborate with internal research and engineering teams to adapt and validate state-of-the-art RL methods and optimizations for NVIDIA platform.
  • •Provide technical guidance to customers on integrating NVIDIA RL technologies into their AI workflows.
  • •Build reusable toolchains, experiment management workflows, and technical documentation to accelerate internal and customer-facing projects.

Key Requirements

  • •MS or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related field, with strong foundations in algorithms and programming.
  • •5+ years (including research) in reinforcement learning, large language model training, or multimodal learning.
  • •Proficiency in PyTorch and familiarity with RL training frameworks and workflows.
  • •Strong engineering skills in distributed training, task orchestration, or evaluation pipelines.
  • •Excellent verbal and written communication skills, with ability to work independently and run exploratory experiments.
Experience:5+ yearsReinforcement learningLarge language modelsMultimodal learningAIHPC
Education:Master's in Computer Science, Artificial Intelligence, Mathematics
Skills:Independent workCommunicationCollaborationProblem-solvingExploratory experimentation
Tech Stack:PyTorchReinforcement LearningLarge Language ModelsMultimodal ModelsCUDAGPUHPCCloudDistributed trainingEvaluation pipelinesExperiment management workflows

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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