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Senior Applied AI Researcher, Digital Biology

NVIDIA · Tel Aviv-Yafo

Senior 🇬🇧 English
Python C++ PyTorch CUDA distributed training large language models Transformers State Space Models multimodal learning agentic AI frameworks

תיאור המשרה

About the role

NVIDIA Israel is building an applied AI research team at the intersection of artificial intelligence and biology. As a Senior Applied AI Researcher you will lead the design of novel deep‑learning architectures for complex biological data and contribute to open‑source publications and code.

Key responsibilities

  • Conceptualize, design, and implement large‑scale models such as LLMs, Transformers and State Space Models for biological datasets.
  • Develop multimodal learning systems that fuse clinical time‑series, imaging, genomics and text.
  • Create foundational, generative and agentic AI models with multi‑step reasoning and tool‑use capabilities.
  • Build digital twin frameworks that combine mechanistic models, physiological data and AI to simulate disease progression and treatment response.
  • Integrate deep‑learning models with autonomous agents to enable end‑to‑end learning‑planning‑execution pipelines.
  • Evaluate model performance, iterate on designs, and produce high‑quality, distributed training code for large‑scale deployment.

Required profile

  • PhD in Machine Learning, Computer Science, Engineering or a related field.
  • 8+ years of hands‑on experience developing, training and deploying deep‑learning models at scale, including LLMs and generative models.
  • Proven track record of independent research, publications at top conferences and robust solution delivery.
  • Experience leading interdisciplinary collaborations with researchers, bioinformaticians and domain experts.
  • Excellent communication skills and ability to thrive in a fast‑paced research environment.

Required skills

  • Python programming
  • C++ programming
  • PyTorch and/or CUDA development
  • Distributed training, optimization and inference
  • Large Language Models, Transformers, State Space Models
  • Multimodal learning across vision, language, time‑series and genomics
  • Agentic AI frameworks (tool‑augmented, planning‑based or multi‑agent systems)

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Tel Aviv-Yafo