Machine Learning Research Engineer (MLRE) - GPUs

Achira

$120K — $150K *
US-Anywhere
+ 2 other locationsRemote
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Minimum 2 years of professional experience in GPU optimization.
  • Deep understanding of GPU programming fundamentals.
  • Demonstrated optimization work through observable artifacts like GitHub contributions.
  • Experience collaborating on software projects in multi-person teams.

Responsibilities

  • Profile and optimize existing PyTorch and JAX code while ensuring model accuracy and scientific integrity.
  • Develop performance-critical code with CUDA, Triton, and Warp frameworks.
  • Represent the company's needs to NVIDIA and implement their tools in our projects.
  • Collaborate closely with scientists to identify high-impact areas, with travel to working groups in SF and NY.

Benefits

  • Opportunity for full-time remote work with frequent travel to corporate sites.
  • Dynamic work environment with cutting-edge technology in molecular machine learning.
  • Collaboration with leading scientists and industry experts.
  • Encouragement of professional development through attendance at conferences.
Full Job Description
About the Role

We're looking for a rare individual who thrives at the intersection of cutting-edge deep learning architectures and high-performance computing. You will help shape the future of molecular machine learning by engineering high-efficiency implementations of advanced architectures for foundation simulation models, accelerating simulations to the limits of the hardware while maintaining fidelity to the underlying physics.

While we prefer candidates willing to relocate to San Francisco or New York City, we are willing to consider full-time remote candidates of exceptional talent who are willing to travel frequently to our two office sites. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.

What You'll Do
  • Take existing PyTorch and JAX, profile and optimize it without compromising model accuracy, reproducibility, and robustness with respect to scientific objectives.
  • Develop with frameworks like CUDA, Triton, Warp, etc. to accelerate performance critical code sections.
  • Liaise with NVIDIA to represent our needs and implement their tooling in our environment.
  • Work day-to-day with scientists to identify areas of greatest impact, including travel to our SF and NY working groups to collaborate.


About You
  • Engineer with at least two years professional experience in GPU optimization.
  • Deep understanding of GPU programming fundamentals.
  • Solid track record of observable artifacts (e.g., GitHub) showing optimization work.
  • Experience collaborating on software projects across multi-person teams.


Nice to Have

Even if you hit none of these bonus features, we encourage you to apply!
  • Experience working with multi-cloud distributed compute systems.
  • Experience working with multi-site distributed company team.
  • Experience working with equivariant architectures that operate on 3-D point clouds.

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