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Senior Research Software Engineer, AI for Science at Microsoft
Berlin, England, Germany -
Full Time

Start Date

Immediate

Expiry Date

27 Feb, 26

Salary

0.0

Posted On

29 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Deep Learning, High Performance Computing, Python, C++, Fortran, Open-Source Software, CUDA Programming, Numerical Methods, Quantum Chemistry, DFT Software, Interdisciplinary Collaboration, Technical Communication

Industry

Software Development

Description
Collaborate with internal and external parties on integrating our deep learning models in high performance DFT software frameworks, targeting both CPU and GPU-based frameworks Prepare and maintain open-source releases and releases for beta testers Write custom efficient GPU implementations for our deep learning models Work cross-functionally with deep learning and quantum chemistry researchers and engineers to align model development strategies with high-performance integration into CPU and GPU-based DFT software frameworks. MSc in computer science, mathematics, physics, chemistry, or a related area Proficiency in collaborative software engineering in Python and in C++ or Fortran Experience with maintenance of open-source libraries or commercial software packages Understanding CPU and GPU compute architecture fundamentals. Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds PhD degree in computer science, mathematics, physics, chemistry, or a related area or comparable industry experience Experience with CUDA programming. Experience with developing and optimizing numerical methods for high-performance computing platforms. Experience with development of high-performance DFT or quantum chemistry software.
Responsibilities
Collaborate with internal and external parties to integrate deep learning models into high-performance DFT software frameworks. Prepare and maintain open-source releases and write custom efficient GPU implementations for deep learning models.