Applied Machine Learning Scientist

Cerebras Systems-posted 8 months ago
$190,000 - $205,000/Yr
Full-time • Entry Level
Remote • Sunnyvale, CA
Professional, Scientific, and Technical Services
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Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. Cerebras Systems Inc. has multiple openings for Applied Machine Learning Scientist.

  • Survey scientific literature for state-of-the-art machine learning models, such as computer vision (CV), natural language processing (NLP), and generative models, and implement these models using Python and PyTorch.
  • Design machine learning solutions for computer vision, NLP, and multimodal domains to address business challenges, and train these machine learning models on Cerebras hardware.
  • Investigate and analyze the working principles and training dynamics of machine learning models using linear algebra and optimization methods.
  • Evaluate machine learning model results with tools like NumPy, scikit-learn, pandas, and matplotlib.
  • Curate high-quality datasets and optimize data compositions for machine learning models by writing Python and PyTorch scripts to generate datasets and load data for training machine learning models.
  • Develop, test, and maintain the machine learning models and training code using modern tools like Git, test frameworks such as PyTest, and documentation platforms like Jira.
  • Master's degree or foreign equivalent degree in Electrical Engineering, Computer Science, Computer Engineering, or a related field.
  • Demonstrated knowledge of machine learning algorithms, including computer vision, natural language processing, generative models, and their architectures and training methods.
  • Machine learning framework: PyTorch.
  • Programming with Python.
  • Using Linux, BASH and Git.
  • Demonstrated knowledge of Mathematical foundations of machine learning theory, linear algebra, and optimization.
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