Applied Deep Learning Scientist, Geometric Deep Learning
About The Position
NVIDIA is using the power of high-performance computing and AI to accelerate digital biology. We are seeking passionate and hardworking individuals to help us realize our mission. As an Applied Deep Learning Scientist, Geometric Deep Learning, you will join a research and development team enthusiastic about infrastructure development and partnerships with industry and academia. This opportunity involves researching, implementing, productizing, and delivering deep learning algorithms for atomistic modeling, life sciences, drug discovery, and materials science. The team carries out applied research and contributes to productizing the results. What makes this opportunity outstanding is the chance to work at the forefront of AI and computational science, making significant contributions to fields that impact the world. You will be part of an ambitious team driving innovation and pushing the boundaries of what's possible! What you will be doing: Develop and refine deep learning algorithms related to geometric deep learning in the biological and materials sciences Build metrics for and assist with the evaluation of model predictions and results Stay on top of recent research and discover methods to harness new advancements, either as applied research initiatives or by directly embedding them into product development Collaborate with multiple AI infrastructure and research teams Seek opportunities to incorporate advances in the field and other NVIDIA products into our infrastructure
Requirements
- 5+ years of relevant experience
- Completed a MS or PhD Degree in a quantitative field such as Statistics, Physics, Computational Biology, Computer Science, Mathematics (or a related field), or equivalent experience
- Expertise in deep learning and machine learning
- Strong experience with Python for deep learning (PyTorch, Jax, Warp) and relevant specialized deep learning libraries (e.g., PyG, cuEquivariance, e3nn)
- Experience with modeling and validation of protein sequences and/or protein structures and related tools
- Recognition for technical leadership contributions, capable of self-direction, and willingness to learn from and guide others
- Strong communication skills, organized and self-motivated, a phenomenal teammate
Nice To Haves
- Knowledge of recent developments in geometric and/or generative deep learning models applied to biological and materials sciences, including AlphaFold3, BioEmu, GNoMe
- Background with protein or small molecule or material simulation tools that use atomistic or coarse-grained interaction models such as OpenMM, GROMACS, LAMMPS, TorchSim
- Experience with C/C++, CUDA, docker
- Experience with open-source development
- Relevant publication history and/or conference attendance
Responsibilities
- Develop and refine deep learning algorithms related to geometric deep learning in the biological and materials sciences
- Build metrics for and assist with the evaluation of model predictions and results
- Stay on top of recent research and discover methods to harness new advancements, either as applied research initiatives or by directly embedding them into product development
- Collaborate with multiple AI infrastructure and research teams
- Seek opportunities to incorporate advances in the field and other NVIDIA products into our infrastructure
Benefits
- You will also be eligible for equity and benefits
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
5,001-10,000 employees
