Description
NVIDIA seeks a motivated AI Researcher to join the BioNemo/TAO R&D team in Hanoi or Ho Chi Minh city.
In this position, you will invent, develop, and test innovative deep learning and generative AI models , such as diffusion models, graph neural networks, and large-scale foundation models , targeting critical scientific problems in drug discovery, urban planning, and warehouse operations.
You will work where advanced AI research meets life sciences and smart environments, building models that extend AI’s potential to improve human health and intelligent urban warehouses.
Collaborating with top researchers, engineers, and diverse teams, you will develop NVIDIA's BioNemo/TAO platform further.
You will transform innovative research into production-ready solutions coordinated across NVIDIA SDKs, Inference Microservices (NIM), and practical scientific workflows.
This position offers scientists a chance to use AI to accelerate science and positively affect human health.
Responsibilities
- Investigate, design, and optimize deep learning models , including diffusion models, flow-matching networks, transformer architectures, and graph neural networks , for structured prediction tasks in molecular and protein science.
- Build and implement generative AI systems for various user cases.
- Contribute to the development and scaling of foundation models for several domain applications, including protein structure, molecular interaction, and other fields.
- Apply and adapt NVIDIA acceleration techniques to deploy large-scale AI models on NVIDIA GPU hardware with high efficiency and accuracy.
- Conduct detailed experiments, ablation studies, and benchmarking to evaluate model accuracy, robustness, generalization, and scalability across diverse datasets.
- Work together with multi-functional groups to incorporate research findings into the BioNemo SDK, NVIDIA production pipelines, and customer-facing scientific workflows.
- Build and improve post-processing and evaluation pipelines that transform raw model predictions into actionable insights for downstream tasks.
- Participate actively in research discussions, paper reading groups, code reviews, and technical documentation to share knowledge and elevate team methodology.
Requirements
- Bachelor's (Honours), MS, or PhD or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a closely related field;
- 5+ years of experience in machine learning and deep learning, with hands-on experience in PyTorch or equivalent frameworks.
- Strong grasp of generative models, transformer architectures, or graph neural networks for structured or geometric data.
- Proficiency in Python and experience managing reproducible ML experiments.
- Excellent analytical and problem-solving skills with the ability to implement and iterate complex model architectures.
- Strong communication skills and a collaborative attitude suited to a fast-paced, investigation-focused team environment.
Nice to Have
- US patents, publications or preprints at venues such as NeurIPS, ICML, ICLR, AAAI, CVPR, or Nature/IEEE family journals.
- Experience with equivariant graph neural networks or SE(3)/E(3)-equivariant frameworks for 3D geometry modelling.
- Research or project experience in computational drug discovery or life sciences.
- Familiarity with NVIDIA frameworks such as BioNemo, CuEquivariance, TAO, Aerial 5G/6G.