Senior Applied AI Researcher, Digital Biology

NVIDIA Israel is seeking innovative and driven Applied Researchers in the field of deep learning. We are looking for candidates with a consistent track record of research excellence; a broad perspective across the fields of artificial intelligence; and deep experience in machine learning, applied science, and computational medicine. NVIDIA is a world leader in high-performance and mobile computing technology for AI, with ambitious plans for future systems. This position offers the opportunity to have a real impact in an applied research-focused team in a dynamic company.

The Applied AI Architecture group focuses on applied research. As a member of the team, you will publish your work and share code as an open source. In addition, you will join a groundbreaking initiative at the intersection of AI and biology. This collaborative project aims to develop both foundational and generative AI models, as well as agentic AI systems, redefining our understanding of complex biological systems and enabling advanced diagnostics and novel scientific insights, while strengthening NVIDIA’s Health Platform ecosystem.

• Conceptualize, design, and implement novel deep learning architectures for biological data, with a strong focus on large-scale models such as Large Language Models (LLMs), Transformers, and State Space Models (SSMs).
• Develop multimodal learning systems that integrate heterogeneous data types (e.g., clinical time-series, imaging, genomics, and text) for improved representation and prediction.
• Develop both foundational and generative models and agentic AI systems, including multi-step reasoning, tool use, and autonomous decision-making capabilities.
• Develop digital twin systems for healthcare, combining mechanistic models, physiological data, and AI to simulate disease progression, treatment response, and patient-specific trajectories.
• Implement deep learning systems integrated with agents, enabling end-to-end workflows that combine learning, planning, and execution.
• Evaluate model performance, analyze results, and iterate on designs to achieve optimal outcomes.
• Apply your knowledge of distributed training to build high-quality code for training, optimizing, and deploying large-scale models, while managing complex datasets.
• Collaborate closely with a diverse team of researchers, bioinformaticians, and domain experts in a highly interdisciplinary environment.

We are looking for individuals who demonstrate a strong foundation in deep learning and a proven ability to translate innovative ideas into practical, scalable systems.

• PhD in Machine Learning, Computer Science, Engineering, or a related discipline.
• 8+ years of hands-on experience in developing, training, and deploying deep learning models at scale, including LLMs, Transformers, SSMs, and/or generative models.
• Experience with multimodal learning and integrating diverse data modalities is highly valued.
• Experience with agentic AI frameworks or systems (e.g., tool-augmented models, planning-based agents, or multi-agent systems) is a strong advantage.
• Strong expertise in distributed training, optimization, and inference.
• Proven ability to lead independent research, implement robust solutions, and rigorously evaluate performance.
• Track record of publications and presentations at top conferences.
• Strong programming skills in Python and C++, with experience in PyTorch and/or CUDA.
• Excellent communication skills and ability to thrive in a dynamic, research-driven team. Mentoring experience is a plus.

Ways To Stand Out From The Crowd

• Hands-on experience building advanced AI systems, including agentic AI (RAG, tools, planning, multi-agent) and multimodal models combining vision, language, and structured/time-series data.
• Proven track record optimizing large-scale ML systems, with experience in data pipelines and distributed frameworks for LLM-scale data.
• Background in bioinformatics or digital biology, with experience working across interdisciplinary teams spanning research, engineering, and clinical domains.
• Experience in building or implementing digital twin systems, simulation frameworks, or data-driven modeling in healthcare or related domains is a strong plus.