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GPU Engineer

About the role:

As a GPU Engineer at Bioptimus, you will have the opportunity to improve medical research using state-of-the-art machine learning algorithms. You will work with interdisciplinary teams with both machine learning and biomedical expertise to build foundation models of biology that will unlock AI applications in biomedical innovation.

In particular, you will:

As an expert GPU Engineer with experience in large-scale deep learning, you will:

  • Join a team setting the foundations and paving the way for the development and application of foundation models in biomedical research
  • Drive the implementation and application of advanced machine learning methods, in particular in foundation models, representation learning, large language models and generative AI, to relevant internal and external projects
  • Contribute to methodological research in key areas to unlock novel applications in collaboration with research scientists.

Depending on your level of experience, you will have the opportunity to supervise a team and lead ambitious projects.

Who we are looking for:

The successful GPU Engineer will have a ‘team-first’ kind of attitude; be independent, curious and detail-attentive; thrive in a dynamic, fast-paced environment; and be fun to work with.

And

  • Expert in crafting high-performance CUDA kernels to unleash the full potential of GPUs. Possesses deep knowledge of distributed computing frameworks for managing current-generation GPU clusters.
  • Extracting maximum performance from modern GPUs (like H100) for efficient model inference and training, by crafting low-level code and redesigning architectural elements.
  • Integrating low-level efficient code in a high-level MLOps framework
  • Overall understanding of the field of generative AI, knowledge or interest in fine-tuning and using language models for applications
  • Demonstrated practical expertise in representation learning/large language models/generative AI, including prior experience of implementing, training and/or deploying large neural networks
  • Strong analytical skills
  • Excellent command of coding in python, and excellent grasp of programming best practices
  • Passionate about the intersections of healthcare/biology and AI
  • MSc or PhD in Computer Science-related field (machine learning, applied mathematics, computer science, software engineering) or equivalent experience;
  • Strong expertise with deep learning frameworks (Tensorflow, PyTorch, Jax, etc.) : you should already master one of these frameworks and implemented large neural networks in an industrial environment or equivalent
  • Excellent written and oral communication skills
  • Highly organized and meticulous about details; capable of tackling complex problems logically and effectively

Ways to stand out:

The following are not necessarily required, but would be a plus for the GPU Engineer:

  • Experience in biological data analysis: proteins, bulk or single-cell omics, histopathology, etc
  • Extensive post-master or good level of post-doctoral experience; or the equivalent experience industry experience
  • Experience managing multi-stakeholder research projects
  • Publications in high-impact journals (JMLR, TMLR, ...) and conferences (NeurIPS, ICLR, ICML, …) 

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