Applied Machine Learning Engineering Intern

Cerebras is developing a radically new chip and system to dramatically accelerate deep learning applications. Our system runs training and inference workloads orders of magnitude faster than contemporary machines, fundamentally changing the way ML researchers work and pursue AI innovation.

We are innovating at every level of the stack – from chip, to microcode, to power delivery and cooling, to new algorithms and network architectures at the cutting edge of ML research. Our fully-integrated system delivers unprecedented performance because it is built from the ground up for the deep learning workload.

Cerebras is building a team of exceptional people to work together on big problems. Join us!

The Role

As an applied machine learning engineering intern, you will take today’s state-of-the-art solutions in various verticals and adapt them to run on the new Cerebras system architecture.

Specific responsibilities for this position include:

  • Working with language modeling, sentiment analysis and statistical machine translation and optimizing them for the Cerebras stack.
  • Designing automatic speech recognition systems using algorithms such as WaveNet and WaveRNN, and augmenting them with Cerebras-specific optimizations.
  • Implementing solutions for verticals such as computer vision for image classification, object localization, autonomous driving, medical image analysis.
  • Working with the Cerebras research team to incorporate novel algorithms into CNN and RNN models.
  • Working with customers to optimize existing models for the Cerebras stack, and develop new approaches for solving real world AI problems.

This role will allow you to work closely with partner companies at the forefront of their fields across many industries. You will get to see how deep learning is being applied to some of the world’s most difficult problems today and help ML researchers in these fields to innovate more rapidly and in ways that are not currently possible on other hardware systems.

Skills & Qualifications

  • Graduate and undergraduate students with a background in Deep Learning and Neural Networks
  • Familiarity with TensorFlow, PyTorch or Caffe, with a good understanding of how to define custom layers and backpropagate through them.
  • Experience with supervised deep learning models such as RNNs and CNNs.
  • Experience in vertical such as computer vision, language modeling or speech recognition.

Location

Our cozy and well-appointed headquarters are in the heart of Silicon Valley near downtown Los Altos, California.

Our beautiful San Diego offices overlook views of the Sorrento Valley canyon.

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