Senior AI Researcher - BioNemo and TAO
Indexed description
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.
What You'll Be Doing
- 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. These include molecular building, structural prediction, and image/video abnormal generation. The focus is on producing reliable, ranked outputs for scientific and real-world use.
- 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 (e.g., CuEquivariance, efficient attention, mixed-precision inference) 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 (e.g., NVIDIA Inference Microservices — NIM), 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.
- Bachelor's (Honours), MS, or PhD or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a closely related field; recent post-graduates are strongly encouraged to apply.
- 5+ years of experience in machine learning and deep learning, with hands-on experience in PyTorch or equivalent frameworks.
- Strong grasp of generative models (diffusion models, flow matching, VAEs), transformer architectures, or graph neural networks for structured or geometric data.
- Proficiency in Python and experience managing reproducible ML experiments (e.g., with Hydra, WandB, or similar tooling).
- 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.
- 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 (such as Equivariant Graph Attention 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.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com
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