Applied Atomistic Modeling Researcher, Drug Discovery

NVIDIA•Santa Clara, CA

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 Atomistic Modeling Researcher for Drug Discovery, 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 in drug discovery. 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 with strong partnerships in the wet lab, 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 machine learning algorithms related to atomistic modeling in drug discovery 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 What we need to see: 5+ years of relevant experience PhD Degree in a quantitative field such as Computer Science, Physics, Computational Biology, Mathematics (or a related field), or equivalent experience Expertise in atomistic modeling and machine learning use cases for it, such as free energy modeling, machine learnt interatomic force fields, conformer generation Strong experience with Python for deep learning (PyTorch, Jax, Warp) and relevant specialized deep learning libraries (e.g., PyG, cuEquivariance) Recognition for technical leadership contributions, capable of self-direction, and willingness to learn from and guide others Strong communication skills and self-motivation Ways to stand out from the crowd: Knowledge of recent developments in geometric and/or generative deep learning models applied to computational biology, such as AlphaFold3, BioEmu Background with protein or small molecule simulation tools that use atomistic or coarse-grained interaction models such as OpenMM, GROMACS, TorchSim, JAX-MD Experience with C/C++, CUDA, docker Contributions to open-source development Relevant publication history and/or conference attendance Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 192,000 USD - 304,750 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until February 21, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. NVIDIA is the world leader in accelerated computing. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society. Learn more about NVIDIA.

Requirements

5+ years of relevant experience
PhD Degree in a quantitative field such as Computer Science, Physics, Computational Biology, Mathematics (or a related field), or equivalent experience
Expertise in atomistic modeling and machine learning use cases for it, such as free energy modeling, machine learnt interatomic force fields, conformer generation
Strong experience with Python for deep learning (PyTorch, Jax, Warp) and relevant specialized deep learning libraries (e.g., PyG, cuEquivariance)
Recognition for technical leadership contributions, capable of self-direction, and willingness to learn from and guide others
Strong communication skills and self-motivation

Nice To Haves

Knowledge of recent developments in geometric and/or generative deep learning models applied to computational biology, such as AlphaFold3, BioEmu
Background with protein or small molecule simulation tools that use atomistic or coarse-grained interaction models such as OpenMM, GROMACS, TorchSim, JAX-MD
Experience with C/C++, CUDA, docker
Contributions to open-source development
Relevant publication history and/or conference attendance

Responsibilities

Develop and refine machine learning algorithms related to atomistic modeling in drug discovery
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