Posted at: 18 February
Applied Atomistic Modeling Researcher, Drug Discovery
Company
NVIDIA Corporation is a Santa Clara-based technology company specializing in designing GPUs and AI solutions for gaming, professional visualization, and cloud services, operating in both B2B and B2C markets globally.
Remote Hiring Policy:
NVIDIA supports flexible remote work arrangements and hires from various regions globally, including the Americas, Europe, Asia, and the Middle East, with roles that may require collaboration across time zones.
Job Type
Full-time
Allowed Applicant Locations
United States
Salary
$192,000 to $304,750 per year
Job Description
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 discoveryBuild metrics for and assist with the evaluation of model predictions and resultsStay on top of recent research and discover methods to harness new advancements, either as applied research initiatives or by directly embedding them into product developmentCollaborate with multiple AI infrastructure and research teamsSeek opportunities to incorporate advances in the field and other NVIDIA products into our infrastructure What we need to see:5+ years of relevant experiencePhD Degree in a quantitative field such as Computer Science, Physics, Computational Biology, Mathematics (or a related field), or equivalent experienceExpertise in atomistic modeling and machine learning use cases for it, such as free energy modeling, machine learnt interatomic force fields, conformer generationStrong 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 othersStrong 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, BioEmuBackground with protein or small molecule simulation tools that use atomistic or coarse-grained interaction models such as OpenMM, GROMACS, TorchSim, JAX-MDExperience with C/C++, CUDA, dockerContributions to open-source developmentRelevant publication history and/or conference attendanceYour 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.