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NVIDIA

Designs the chips and software that power AI

Deep Learning Solution Architect - RL and Post-training

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBeijing, ChinaNo salary listedIn-person

About the Job

Requirements

  • A Master of Science or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related field, with solid foundations in algorithms and programming.
  • At least 5 years of experience, including research, in Reinforcement Learning, Large Language Model training, or multimodal learning.
  • Proficiency in PyTorch and familiarity with Reinforcement Learning training frameworks and workflows.
  • Engineering experience with distributed training, task orchestration, or evaluation pipelines.
  • Ability to work independently with minimal day-to-day direction and willingness to conduct exploratory experiments on frontier problems.
  • Desire to be involved in multiple diverse and innovative projects.
  • Strong verbal and written communication skills.

Responsibilities

  • Drive the development and optimization of Reinforcement Learning algorithms and infrastructure for Large Language Models and multimodal models.
  • Develop and enable support for open-source models in NVIDIA Reinforcement Learning and post-training frameworks.
  • Collaborate with internal research and engineering teams to adapt and validate state-of-the-art Reinforcement Learning methods and optimizations for the NVIDIA platform.
  • Improve Reinforcement Learning initiatives and customer engagements by providing technical guidance on integrating NVIDIA Reinforcement Learning technologies into customers’ AI workflows.
  • Develop and maintain reusable toolchains, experiment management workflows, and technical documentation to accelerate internal and customer-facing projects.

Desired Qualifications

  • Experience with RLHF, GRPO, DPO, or other alignment and post-training methods for Large Language Models.
  • Experience with scale-out HPC or cloud architectures for large-scale model training.
  • CUDA optimization or GPU performance-tuning experience.
  • Experience with agentic AI systems, code-generation models, or multimodal Reinforcement Learning.
  • Publications in top-tier venues in Reinforcement Learning, NLP, or multimodal learning.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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