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NVIDIA

Designs the chips and software that power AI

Senior Deep Learning Scientist - Multimodal Agentic RL

AI ResearchConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A Master's degree (or equivalent experience) or PhD in Computer Science, AI, or Applied Math, with 8+ years of relevant work experience.
  • Excellent programming skills in Python and strong fundamentals in scalable model development and deep learning frameworks such as PyTorch.
  • Strong knowledge of machine learning and deep learning techniques and modern foundation model architectures, including Transformers and mixture-of-experts models.
  • Foundational understanding of reinforcement learning algorithms and implementation, including Markov decision processes, policies, and reward design.
  • Hands-on experience post-training multimodal models for omni-modality audio-visual reasoning, full-duplex voice chat, and human-AI interaction.
  • Proven ability to manage model development life cycles, including dataset versioning, experiment tracking, and evaluation pipelines.

Responsibilities

  • Apply fundamental and applied research to develop, train, fine-tune, and deploy large language models for agentic systems involving audio-visual reasoning, tool usage, and document understanding.
  • Advance post-training and alignment methods, including instruction tuning, preference optimization, reinforcement learning from human feedback, reinforcement learning from verifiable rewards, and MOPD, to improve multimodal agents for complex use cases.
  • Research and develop agentic reasoning and grounded perception capabilities, focusing on planning, tool execution, and long-horizon task completion across digital and physical environments.
  • Lead the collection, development, and benchmarking of multimodal datasets, ensuring high-quality evaluation of model accuracy, safety, and task-completion success.

Desired Qualifications

  • A strong record of publications in top-tier AI and machine learning venues such as NeurIPS, ICML, ICLR, or CVPR.
  • Validated experience training and deploying multimodal foundation models using large-scale distributed infrastructure.
  • Experience applying deep reinforcement learning techniques to train multimodal agents in complex simulation or gaming environments.
  • A background in audio or speech AI, especially audio language models or audio generation.
  • A background in building embodied AI systems that integrate multimodal perception with backend action-fulfillment and long-horizon planning.

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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