Senior Architect- Molecular Dynamics

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

NVIDIA is seeking a Senior Architect with extensive experience in molecular dynamics to develop and deploy functional forms for modern forcefields, prototype and implement enhanced sampling methods, and integrate libraries with FEP, MD, and forcefield fitting workflows. The role involves building GPU-friendly algorithms with the CUDA-X library team to accelerate kernels in MD.

What you'd actually do

  1. Developing and deploying functional forms for modern forcefields. This includes implicit solvent, explicit solvent, and polarizable models. These forms are enabled for both classical and machine-learned interatomic potentials.
  2. Prototyping and implementing enhanced sampling methods, including but not limited to: Gibbs sampling, umbrella sampling, REST2, SAMS, lambda dynamics, r-RESPA, and machine-learned samplers.
  3. Integrating our libraries with various FEP, MD, and forcefield fitting workflows. Some relevant examples are: relative and absolute binding free energy calculations, equilibrium and non-equilibrium protocols, fitting forcefields in the context of both ab initio QM data and condensed phase data.
  4. Working with our CUDA-X library team and other engineering teams to build GPU-friendly algorithms to ultimately accelerate the hardest and most time-consuming kernels in MD and making them widely accessible to developers around the world.

Skills

Required

  • Master’s or Ph.D. in Computer Science, Chemistry, Physics, or a related field (or equivalent experience)
  • 12+ years of experience of methods development in MD, forcefield parameterization, and/or free energy methods
  • Proficiency in Python, C++, and/or CUDA
  • established best-practices such as code-reviews, unit testing, integration testing
  • deep understanding of trade-offs between practicality, generalizability, and performance of the solutions, and articulating them to both technical and non-technical experts

Nice to have

  • Meaningful contributions to a major MD engine. (e.g. OpenMM, GROMACS, AMBER, etc.)
  • Experience with ML frameworks such as jax, pytorch, or tensorflow and the machinery behind automatic differentiation.
  • Numerical analysis and methods development (e.g. quantifying error propagation, mixed precision and fixed precision modes, bitwise determinism, etc.).
  • Track record of starting and landing initiatives that span engineering, product, and/or research, etc.

What the JD emphasized

  • extensive experience in molecular dynamics
  • push the boundaries of scaling laws
  • build broadly applicable primitives
  • 12+ years of experience of methods development in MD, forcefield parameterization, and/or free energy methods
  • Proficiency in Python, C++, and/or CUDA
  • deep understanding of trade-offs

Other signals

  • machine-learned interatomic potentials
  • machine-learned samplers
  • GPU-friendly algorithms
  • CUDA-X library