Senior AI-Driven Materials & Chemistry Apps Eng Equity

NVIDIA Corporation; Santa Clara (CA); On-site; USD 168,000 - 322,000; Full time

Benefits offered by this job

Equity

Benefits package

Job summary

NVIDIA Corporation in Santa Clara, CA, is seeking a senior application engineer to accelerate AI-driven simulations for materials discovery within the ALCHEMI platform. You will translate research into production-ready software used by scientists, interfacing with research, engineering, and product teams.

You will work on high-performance numerical methods for materials science, collaborate across disciplines, and contribute to benchmarks, whitepapers, and workshops.

Qualifications

• PhD or equivalent in computational physics, chemistry, CS or related fields.
• 8+ years of experience in high-performance numerical methods for scientific computing or materials applications.
• Experience running material science simulations on large-scale HPC systems.
• Ability to work independently and in globally distributed teams; proficiency in Fortran, C++, CUDA, Python.

Responsibilities

• Collaborate with researchers and AI teams to develop NVIDIA ALCHEMI tools for atomistic workflows.
• Guide customers/developers on integrating material science apps with AI surrogates in workflows.
• Profile applications and run benchmarks to demonstrate value and performance.
• Collaborate with experts on whitepapers and research.
• Engage in workshops at the intersection of materials science and AI.

Skills

Fortran

C++

CUDA

Python

HPC

Education

PhD or equivalent in computational physics, computational chemistry, computer science, or related technical fields

Tools

Material Science tools

Job description

NVIDIA Corporation in Santa Clara, CA, is seeking a senior application engineer to accelerate AI-driven simulations for materials discovery within the ALCHEMI platform. You will translate research into production-ready software used by scientists, interfacing with research, engineering, and product teams.

You will work on high-performance numerical methods for materials science, collaborate across disciplines, and contribute to benchmarks, whitepapers, and workshops.