Applied Machine Learning Engineer
Cerebras is developing a radically new chip and system to dramatically accelerate deep learning applications. Our system takes today’s training times and inference latencies and reduces them by orders of magnitude, 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!
As an applied machine learning engineer, 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:
- Implementing solutions for verticals such as computer vision for image classification, object localization, autonomous driving, medical image analysis.
- 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.
- 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.
Cerebras is hiring full time team members as well as interns.
Skills & Qualifications
- Masters or PhD in Computer Science or related field.
- 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.