Senior Deep Learning Scientist, Multimodal Agentic RL

Details

Role description

NVIDIA is hiring a Senior Deep Learning Scientist to advance efforts in streaming and agentic multimodal AI.

You will demonstrate foundational expertise in deep learning, reinforcement learning, and applied mathematics to help develop models capable of reasoning, planning, and acting across diverse modalities. This role focuses on defining core algorithmic improvements for foundational models that balance multiple data types, scaling ideas through NVIDIA’s Nemotron platform.

Responsibilities

• Apply fundamental and applied research to develop, train, fine-tune, and deploy advanced neural networks for language processing in agentic systems encompassing audio-visual reasoning, tool usage, and document understanding.
• Advance post-training and alignment methods including instruction tuning, preference optimization, and RLHF/RLVR 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.

Requirements

• 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 with strong fundamentals in scalable model development and deep learning frameworks like PyTorch.
• Strong knowledge of ML/DL techniques and modern foundation model architectures, including Transformers and mixture-of-experts models.
• Foundational understanding of reinforcement learning algorithms and implementation, including MDPs, policies, and reward design.
• Hands-on experience in post-training multimodal models for audio-visual reasoning and human-AI interaction.
• Proven ability to manage model development life cycles, including dataset versioning, experiment tracking, and evaluation pipelines.

Ways to stand out

• 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.
• Background in building embodied AI systems that integrate multimodal perception with backend action-fulfillment and long-horizon planning.