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GenBio AI

Posted 1 month ago

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Research Engineer Intern

ParisOn-siteInternship

AI Summary

Research Engineer Intern works with AI, foundation models, and computational biology to design, run, and analyze large-scale experiments, improving models and contributing to biology-focused AI systems.

About this role

Headquartered in Silicon Valley, we are a newly established start-up where a collective of visionary scientists, engineers, and entrepreneurs are dedicated to transforming the landscape of biology and medicine through the power of generative AI. Our team comprises leading minds and innovators in AI and biological science, pushing the boundaries of what is possible. We are dreamers who reimagine a new paradigm for biology and medicine.
We are committed to decoding biology holistically and enabling the next generation of life-transforming solutions. As the first mover in pan-modal Large Biological Models (LBM), we are pioneering a new era of biomedicine, with our LBM training leading to ground-breaking advancements and a transformative approach to healthcare. Our robust R&D team and leadership in LLMs and generative AI position us at the forefront of this revolutionary field. With headquarters in Silicon Valley, California, and a branch office in Paris and Abu Dhabi, we are poised to make a global impact. Join us as we embark on this journey to redefine the future of biology and medicine through the transformative power of Generative AI.

Job Description:

  • You will work with the team to conduct cutting-edge research in AI, foundation models, and computational biology. Your primary tasks will include improving existing models and exploring new methodologies to advance our AI capabilities in biology.
  • You will collaborate with the team on designing and executing large-scale experiments, analyzing complex datasets, and applying statistical techniques to validate the performance and robustness of AI systems.
  • Additionally, you will work closely with AI/machine learning researchers and computational biologists to develop Genbio AI’s state-of-the-art biology foundation models and drive the research agenda to generate impact.

    • Qualification:

  • Currently enrolled in a full-time master's or PhD (preferred) program in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field (preferably in the United States).
  • A strong coder with excellent skills in C/C++ and Python.
  • Fluent in deep learning frameworks like PyTorch (and/or JAX), Hugging Face (Datasets, Accelerate, Transformers, etc.), Megatron-LM, DeepSpeed, etc.
  • Have a solid understanding of GPU, CPU, or other AI accelerator architectures.
  • Familiar with LLM (and/or other foundation model) architectures (such as attention mechanisms, state-space models, MoE, etc.) and training infrastructure (e.g., large-scale GPU clusters).
  • Have experience improving ML accuracy using low-precision formats.
  • Have 1+ years of relevant industry experience.
  • Derive a great deal of satisfaction from every percentage point of performance improvement.
  • Have experience writing and optimizing compute kernels using CUDA or similar languages.

    • Nice to Have:

  • Current PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing, Compilers, or other systems.
  • Co-optimizing computing infrastructure and deep learning frameworks for optimal performance on specific workloads. Identify and resolve performance bottlenecks through profiling and system analysis.
  • Experience collaborating with data scientists and machine learning engineers to integrate distributed training capabilities into GenBio AI’s model development and deployment frameworks.
  • Proficient in Python with experience in GPU-accelerated libraries (e.g., CUDA, cuDNN).
  • Knowledge of performance profiling and optimization tools for HPC and deep learning.

    • Skills

    Attention MechanismsC/C++Compute KernelsCPU ArchitecturesCUDACUDA Or Similar LanguagesCuDNNDeepSpeedGPU ArchitecturesHugging FaceJAXLarge-scale GPU ClustersLLM ArchitecturesMegatron-LMMoEPythonPyTorchState Space ModelsTraining Infrastructure

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