Senior Applied AI Researcher, Digital Biology

at Nvidia

USD 184,000-356,500 per year

SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 7 Agentic AI @ 7 CUDA @ 4 Data Pipelines @ 4 Deep Learning @ 7 LLM @ 4 Machine Learning @ 7 PyTorch @ 4 Python @ 4 RAG @ 4

Conceptualize, build, and implement novel deep learning architectures for biological data, focusing on large-scale models like 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 foundational and generative models along with agentic AI systems, including multi-step reasoning, tool use, and autonomous decision-making.
Develop digital twin systems for healthcare by integrating mechanistic models, physiological data, and AI to simulate disease progression, treatment response, and patient-specific trajectories.
Implement deep learning systems coordinated with agents, enabling end-to-end workflows that combine learning, planning, and execution.
Evaluate model performance, analyze results, and iterate on builds to achieve efficient outcomes.
Apply 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.

Solid background in deep learning and demonstrated capability to turn innovative concepts into practical, scalable systems.
Advanced Degree (MS or PhD) in Machine Learning, Computer Science, Engineering, or a related field (or equivalent experience).
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) and strong expertise in distributed training, optimization, and inference.
Demonstrated capability to conduct independent research, develop effective solutions, and thoroughly assess outcomes.
Proven history of publications and presentations at leading conferences.
Solid programming abilities in Python and C++, along with experience in PyTorch and/or CUDA.

Practical experience developing sophisticated AI systems, including agentic AI (RAG, tools, planning, multi-agent) and multimodal models that integrate vision, language, and structured/time-series data.
Demonstrated success improving large-scale ML systems, along 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 developing or deploying digital twin systems, simulation frameworks, or data-driven modeling in healthcare or related fields is a strong plus.

Base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
Applications for this job will be accepted at least until July 3, 2026.