Machine learning performance scaling researcher

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Optimize machine learning algorithms to scale beyond what is possible today

What you will do

The Compute System Architecture Unit (CSA) at imec is researching emerging workloads and their performance on large-scale supercomputer architectures for next-generation Artificial Intelligence (AI) and high-performance computing (HPC) applications. The team is responsible for algorithm research, runtime management innovations, performance modeling, architecture simulation and prototyping for these future applications and the future systems to execute them, to reach multiple orders of magnitude better performance, energy-efficiency, and total-cost-of-ownership. 

We are looking for the best talents to enable our vision. Are you interested in research in large machine learning applications and networks and making them not only feasible but fast and efficient? Then we’re looking for you! Together with other machine learning experts, high-performance computing experts and computer architects, you will dive in deep in innovative machine learning algorithms that scale to larger application problems and are trained efficiently on future systems. You will work on extrapolating experimental results to make estimations of extremely scaled applications and their efficiency on distributed systems. Your contributions in algorithm research will help to shape the future of machine learning systems.

What we do for you

We offer you the opportunity to join one of the world’s premier research centers in nanotechnology at its headquarters in Leuven, Belgium. With your talent, passion and expertise, you’ll become part of a team that makes the impossible possible. Together, we shape the technology that will determine the society of tomorrow.

We are committed to being an inclusive employer and proud of our open, multicultural, and informal working environment with ample possibilities to take initiative and show responsibility. We commit to supporting and guiding you in this process; not only with words but also with tangible actions. Through, 'our corporate university', we actively invest in your development to further your technical and personal growth. 

We are aware that your valuable contribution makes imec a top player in its field. Your energy and commitment are therefore appreciated by means of a market appropriate salary with many fringe benefits. 

Who you are

  • You have obtained a Master degree or PhD degree in Computer Science or Computer engineering, with experience in machine learning.
  • You have experience in training of large machine learning models in a computational graph framework like pytorch, tensorflow or jax.
  • You have good understanding of state-of-the-art machine learning models and techniques
  • You have experience in creating improvements in machine learning algorithms to not only make them more effective (e.g. higher accuracy), but also more efficient (e.g. less training time, less energy usage) or more scalable (e.g. solving larger problems with less overhead)
  • You have knowledge of distributed machine learning systems and the trends in their hardware architectures
  • Experience with analyzing performance or energy use of machine learning algorithms on hardware is preferred.

Next to domain experience, we are looking for the following elements of behavior:

  • You are able to determine the right tools for the job and can quickly embrace new technological paradigms (languages, libraries, architectures …)
  • You like getting hands-on with the problem
  • You love taking on large challenges and are highly result driven.
  • You can update the team on progress of tasks, seek feedback and are an effective collaborator to your colleagues.
  • Given the international character of imec, a fluent knowledge of English is necessary.

Om tjänsten

Machine learning performance scaling researcher
Kapeldreef 75 Leuven, Belgien
Sista ansökningsdag
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The combination of our widely acclaimed leadership in microchip technology and profound software and ICT expertise is what makes us unique.

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