Senior Deep Learning Scientist, Multimodal Agentic RL
$184k - $288k • Remote • US, CA, Santa Clara
Posted 12d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Research Engineer
About the job
NVIDIA is seeking Senior Deep Learning Scientists to advance their work in streaming and agentic multimodal AI. This role involves developing models capable of reasoning, planning, and acting across diverse modalities, with a focus on defining core algorithmic improvements for multimodal foundation models. You will contribute to high-impact, large language models and multimodal AI products that enhance user experiences, working within the Nemotron LLM team to solve real-world agentic AI challenges.
Responsibilities
- Develop, train, fine-tune, and deploy large language models for agentic systems, including audio-visual reasoning, tool usage, and document understanding.
- Advance post-training and alignment methods such as instruction tuning, preference optimization, and RLHF/RLVR/MOPD for multimodal agents.
- Research and develop agentic reasoning and grounded perception capabilities for planning, tool execution, and long-horizon task completion.
- Lead the collection, development, and benchmarking of multimodal datasets for model evaluation.
- Apply fundamental and applied research to deep learning and reinforcement learning.
- Scale ideas through NVIDIA's Nemotron Omni and VoiceChat platforms.
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.
- Hands-on experience in post-training multimodal models for omni-modality 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.
Benefits
- Equity
- Comprehensive benefits package