Senior Applied Researcher, Foundational AI Models for 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 a depth of 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 researched-focused team in a dynamic company.

The Applied AI Architecture group focuses on applied research; As a research member of the group, you will be publishing your work and share code as open source. In addition, you will join a groundbreaking initiative at the intersection of AI and biology. This collaborative project aims to develop foundational AI models that will redefine our understanding of sophisticated biological systems, paving the way for sophisticated diagnostics and novel insights, and strengthen NVIDIA’s BioNeMo ecosystem.

What You'll Be Doing:

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).

Evaluate model performance, analyse results, and iterate on designs to achieve optimal outcomes.

Apply your knowledge of distributed training to build quality code for training, optimizing, deploying deep learning models and managing large datasets.

Work closely with a diverse team of researchers, bioinformaticians, and domain experts, contributing to a collaborative research environment.

What We Need to See:

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

PhD in Machine Learning: Computer Science, Engineering or related disciplines.

10+ years of hands-on experience in developing, training, and deploying deep learning models, particularly at scale. This includes significant experience with LLMs, Transformers, and SSMs, specifically regarding distributed training, optimization, and inference.

Ability to lead independent research in machine learning, while collaborating with peers. Implement them effectively with robust code and rigorously evaluate their performance. Publish original research and speak at conferences and events.

Programming skills in Python and C++. Fluency in modern deep learning frameworks like PyTorch, or in CUDA is a plus. Practical experience in distributed training.

Strong communication skills along with the ability to work in a dynamic, research-focused team. Mentoring senior engineers and interns a huge plus.

Ways To Stand Out From The Crowd:

A background in Bioinformatics, Genomics, or Pharmaceuticals would be highly valuable.

A proven track record of optimizing complex machine learning systems for performance, cost, and scalability.

Experience with large-scale data pipelines and distributed computing frameworks for preparing and processing LLM-like training data.

Experience working effectively within diverse teams encompassing researchers, engineers, bioinformaticians, and clinicians.

A track record of research excellence with your work published in top conferences and journals such as, NeurIPS, ICML, JMLR, ICLR, CVPR, ICCV, ECCV, AAAI, ACL, EMNLP, Nature.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided a reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.